Detecting real-time human emotions using Open-CV, DeepFace & Python

Hi Guys,

Today, I’ll be using another exciting installment of Computer Vision. Our focus will be on getting a sense of human emotions. Let me explain. This post will demonstrate how to read/detect human emotions by analyzing computer vision videos. We will be using part of a Bengali Movie called “Ganashatru (An enemy of the people)” entirely for educational purposes & also as a tribute to the great legendary director late Satyajit Roy. To know more about him, please click the following link.

Why don’t we see the demo first before jumping into the technical details?

Demo

Architecture:

Let us understand the architecture –

Process Flow

From the above diagram, one can see that the application, which uses both the Open-CV & DeepFace, analyzes individual frames from the source. Then predicts the emotions & adds the label in the target B&W frames. Finally, it creates another video by correctly mixing the source audio.

Python Packages:

Following are the python packages that are necessary to develop this brilliant use case –

pip install deepface
pip install opencv-python
pip install ffpyplayer

CODE:

Let us now understand the code. For this use case, we will only discuss three python scripts. However, we need more than these three. However, we have already discussed them in some of the early posts. Hence, we will skip them here.

  • clsConfig.py (This script will play the video along with audio in sync.)


################################################
#### Written By: SATYAKI DE ####
#### Written On: 15-May-2020 ####
#### Modified On: 22-Apr-2022 ####
#### ####
#### Objective: This script is a config ####
#### file, contains all the keys for ####
#### Machine-Learning & streaming dashboard.####
#### ####
################################################
import os
import platform as pl
class clsConfig(object):
Curr_Path = os.path.dirname(os.path.realpath(__file__))
os_det = pl.system()
if os_det == "Windows":
sep = '\\'
else:
sep = '/'
conf = {
'APP_ID': 1,
'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
'LOG_PATH': Curr_Path + sep + 'log' + sep,
'REPORT_PATH': Curr_Path + sep + 'report',
'FILE_NAME': 'GonoshotruClimax',
'SRC_PATH': Curr_Path + sep + 'data' + sep,
'FINAL_PATH': Curr_Path + sep + 'Target' + sep,
'APP_DESC_1': 'Video Emotion Capture!',
'DEBUG_IND': 'N',
'INIT_PATH': Curr_Path,
'SUBDIR': 'data',
'SEP': sep,
'VIDEO_FILE_EXTN': '.mp4',
'AUDIO_FILE_EXTN': '.mp3',
'IMAGE_FILE_EXTN': '.jpg',
'TITLE': "Gonoshotru – Emotional Analysis"
}

view raw

clsConfig.py

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All the above inputs are generic & used as normal parameters.

  • clsFaceEmotionDetect.py (This python class will track the human emotions after splitting the audio from the video & put that label on top of the video frame.)


##################################################
#### Written By: SATYAKI DE ####
#### Written On: 17-Apr-2022 ####
#### Modified On 20-Apr-2022 ####
#### ####
#### Objective: This python class will ####
#### track the human emotions after splitting ####
#### the audio from the video & put that ####
#### label on top of the video frame. ####
#### ####
##################################################
from imutils.video import FileVideoStream
from imutils.video import FPS
import numpy as np
import imutils
import time
import cv2
from clsConfig import clsConfig as cf
from deepface import DeepFace
import clsL as cl
import subprocess
import sys
import os
# Initiating Log class
l = cl.clsL()
class clsFaceEmotionDetect:
def __init__(self):
self.sep = str(cf.conf['SEP'])
self.Curr_Path = str(cf.conf['INIT_PATH'])
self.FileName = str(cf.conf['FILE_NAME'])
self.VideoFileExtn = str(cf.conf['VIDEO_FILE_EXTN'])
self.ImageFileExtn = str(cf.conf['IMAGE_FILE_EXTN'])
def convert_video_to_audio_ffmpeg(self, video_file, output_ext="mp3"):
try:
"""Converts video to audio directly using `ffmpeg` command
with the help of subprocess module"""
filename, ext = os.path.splitext(video_file)
subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"],
stdout=subprocess.DEVNULL,
stderr=subprocess.STDOUT)
return 0
except Exception as e:
x = str(e)
print('Error: ', x)
return 1
def readEmotion(self, debugInd, var):
try:
sep = self.sep
Curr_Path = self.Curr_Path
FileName = self.FileName
VideoFileExtn = self.VideoFileExtn
ImageFileExtn = self.ImageFileExtn
font = cv2.FONT_HERSHEY_SIMPLEX
# Load Video
videoFile = Curr_Path + sep + 'Video' + sep + FileName + VideoFileExtn
temp_path = Curr_Path + sep + 'Temp' + sep
# Extracting the audio from the source video
x = self.convert_video_to_audio_ffmpeg(videoFile)
if x == 0:
print('Successfully Audio extracted from the source file!')
else:
print('Failed to extract the source audio!')
# Loading the haarcascade xml class
faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# start the file video stream thread and allow the buffer to
# start to fill
print("[INFO] Starting video file thread…")
fvs = FileVideoStream(videoFile).start()
time.sleep(1.0)
cnt = 0
# start the FPS timer
fps = FPS().start()
try:
# loop over frames from the video file stream
while fvs.more():
cnt += 1
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale (while still retaining 3
# channels)
try:
frame = fvs.read()
except Exception as e:
x = str(e)
print('Error: ', x)
frame = imutils.resize(frame, width=720)
cv2.imshow("Gonoshotru – Source", frame)
# Enforce Detection to False will continue the sequence even when there is no face
result = DeepFace.analyze(frame, enforce_detection=False, actions = ['emotion'])
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = np.dstack([frame, frame, frame])
faces = faceCascade.detectMultiScale(image=frame, scaleFactor=1.1, minNeighbors=4, minSize=(80,80), flags=cv2.CASCADE_SCALE_IMAGE)
# Draw a rectangle around the face
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0,255,0), 2)
# Use puttext method for inserting live emotion on video
cv2.putText(frame, result['dominant_emotion'], (50,390), font, 3, (0,0,255), 2, cv2.LINE_4)
# display the size of the queue on the frame
#cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()), (10, 30), font, 0.6, (0, 255, 0), 2)
cv2.imwrite(temp_path+'frame-' + str(cnt) + ImageFileExtn, frame)
# show the frame and update the FPS counter
cv2.imshow("Gonoshotru – Emotional Analysis", frame)
fps.update()
if cv2.waitKey(2) & 0xFF == ord('q'):
break
except Exception as e:
x = str(e)
print('Error: ', x)
print('No more frame exists!')
# stop the timer and display FPS information
fps.stop()
print("[INFO] Elasped Time: {:.2f}".format(fps.elapsed()))
print("[INFO] Approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()
return 0
except Exception as e:
x = str(e)
print('Error: ', x)
return 1

Key snippets from the above scripts –

def convert_video_to_audio_ffmpeg(self, video_file, output_ext="mp3"):
    try:
        """Converts video to audio directly using `ffmpeg` command
        with the help of subprocess module"""
        filename, ext = os.path.splitext(video_file)
        subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"],
                        stdout=subprocess.DEVNULL,
                        stderr=subprocess.STDOUT)

        return 0
    except Exception as e:
        x = str(e)
        print('Error: ', x)

        return 1

The above snippet represents an Audio extraction function that will extract the audio from the source file & store it in the specified directory.

# Loading the haarcascade xml class
faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

Now, Loading is one of the best classes for face detection, which our applications require.

fvs = FileVideoStream(videoFile).start()

Using FileVideoStream will enable our application to process the video faster than cv2.VideoCapture() method.

# start the FPS timer
fps = FPS().start()

The application then invokes the FPS.Start() that will initiate the FPS timer.

# loop over frames from the video file stream
while fvs.more():

The application will check using fvs.more() to find the EOF of the video file. Until then, it will try to read individual frames.

try:
    frame = fvs.read()
except Exception as e:
    x = str(e)
    print('Error: ', x)

The application will read individual frames. In case of any issue, it will capture the correct error without terminating the main program at the beginning. This exception strategy is beneficial when there is no longer any frame to read & yet due to the end frame issue, the entire application throws an error.

frame = imutils.resize(frame, width=720)
cv2.imshow("Gonoshotru - Source", frame)

At this point, the application is resizing the frame for better resolution & performance. Furthermore, identify this video feed as a source.

# Enforce Detection to False will continue the sequence even when there is no face
result = DeepFace.analyze(frame, enforce_detection=False, actions = ['emotion'])

Finally, the application has used the deepface machine-learning API to analyze the subject face & trying to predict its emotions.

frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = np.dstack([frame, frame, frame])

faces = faceCascade.detectMultiScale(image=frame, scaleFactor=1.1, minNeighbors=4, minSize=(80,80), flags=cv2.CASCADE_SCALE_IMAGE)

detectMultiScale function can use to detect the faces. This function will return a rectangle with coordinates (x, y, w, h) around the detected face.

It takes three common arguments — the input image, scaleFactor, and minNeighbours.

scaleFactor specifies how much the image size reduces with each scale. There may be more faces near the camera in a group photo than others. Naturally, such faces would appear more prominent than the ones behind. This factor compensates for that.

minNeighbours specifies how many neighbors each candidate rectangle should have to retain. One may have to tweak these values to get the best results. This parameter specifies the number of neighbors a rectangle should have to be called a face.

# Draw a rectangle around the face
for (x, y, w, h) in faces:
    cv2.rectangle(frame, (x, y), (x + w, y + h), (0,255,0), 2)

As discussed above, the application is now calculating the square’s boundary after receiving the values of x, y, w, & h.

# Use puttext method for inserting live emotion on video
cv2.putText(frame, result['dominant_emotion'], (50,390), font, 3, (0,0,255), 2, cv2.LINE_4)

Finally, capture the dominant emotion from the deepface API & post it on top of the target video.

# display the size of the queue on the frame
cv2.imwrite(temp_path+'frame-' + str(cnt) + ImageFileExtn, frame)

# show the frame and update the FPS counter
cv2.imshow("Gonoshotru - Emotional Analysis", frame)
fps.update()

Also, writing individual frames into a temporary folder, where later they will be consumed & mixed with the source audio.

if cv2.waitKey(2) & 0xFF == ord('q'):
    break

At any given point, if the user wants to quit, the above snippet will allow them by simply pressing either the escape-button or ‘q’-button from the keyboard.

  • clsVideoPlay.py (This script will play the video along with audio in sync.)


###############################################
#### Updated By: SATYAKI DE ####
#### Updated On: 17-Apr-2022 ####
#### ####
#### Objective: This script will play the ####
#### video along with audio in sync. ####
#### ####
###############################################
import os
import platform as pl
import cv2
import numpy as np
import glob
import re
import ffmpeg
import time
from clsConfig import clsConfig as cf
from ffpyplayer.player import MediaPlayer
import logging
os_det = pl.system()
if os_det == "Windows":
sep = '\\'
else:
sep = '/'
class clsVideoPlay:
def __init__(self):
self.fileNmFin = str(cf.conf['FILE_NAME'])
self.final_path = str(cf.conf['FINAL_PATH'])
self.title = str(cf.conf['TITLE'])
self.VideoFileExtn = str(cf.conf['VIDEO_FILE_EXTN'])
def videoP(self, file):
try:
cap = cv2.VideoCapture(file)
player = MediaPlayer(file)
start_time = time.time()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
_, val = player.get_frame(show=False)
if val == 'eof':
break
cv2.imshow(file, frame)
elapsed = (time.time() start_time) * 1000 # msec
play_time = int(cap.get(cv2.CAP_PROP_POS_MSEC))
sleep = max(1, int(play_time elapsed))
if cv2.waitKey(sleep) & 0xFF == ord("q"):
break
player.close_player()
cap.release()
cv2.destroyAllWindows()
return 0
except Exception as e:
x = str(e)
print('Error: ', x)
return 1
def stream(self, dInd, var):
try:
VideoFileExtn = self.VideoFileExtn
fileNmFin = self.fileNmFin + VideoFileExtn
final_path = self.final_path
title = self.title
FullFileName = final_path + fileNmFin
ret = self.videoP(FullFileName)
if ret == 0:
print('Successfully Played the Video!')
return 0
else:
return 1
except Exception as e:
x = str(e)
print('Error: ', x)
return 1

view raw

clsVideoPlay.py

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Let us explore the key snippet –

cap = cv2.VideoCapture(file)
player = MediaPlayer(file)

In the above snippet, the application first reads the video & at the same time, it will create an instance of the MediaPlayer.

play_time = int(cap.get(cv2.CAP_PROP_POS_MSEC))

The application uses cv2.CAP_PROP_POS_MSEC to synchronize video and audio.

  • peopleEmotionRead.py (This is the main calling python script that will invoke the class to initiate the model to read the real-time human emotions from video.)


##################################################
#### Written By: SATYAKI DE ####
#### Written On: 17-Jan-2022 ####
#### Modified On 20-Apr-2022 ####
#### ####
#### Objective: This is the main calling ####
#### python script that will invoke the ####
#### clsFaceEmotionDetect class to initiate ####
#### the model to read the real-time ####
#### human emotions from video or even from ####
#### Web-CAM & predict it continuously. ####
##################################################
# We keep the setup code in a different class as shown below.
import clsFaceEmotionDetect as fed
import clsFrame2Video as fv
import clsVideoPlay as vp
from clsConfig import clsConfig as cf
import datetime
import logging
###############################################
### Global Section ###
###############################################
# Instantiating all the three classes
x1 = fed.clsFaceEmotionDetect()
x2 = fv.clsFrame2Video()
x3 = vp.clsVideoPlay()
###############################################
### End of Global Section ###
###############################################
def main():
try:
# Other useful variables
debugInd = 'Y'
var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
var1 = datetime.datetime.now()
print('Start Time: ', str(var))
# End of useful variables
# Initiating Log Class
general_log_path = str(cf.conf['LOG_PATH'])
# Enabling Logging Info
logging.basicConfig(filename=general_log_path + 'restoreVideo.log', level=logging.INFO)
print('Started Capturing Real-Time Human Emotions!')
# Execute all the pass
r1 = x1.readEmotion(debugInd, var)
r2 = x2.convert2Vid(debugInd, var)
r3 = x3.stream(debugInd, var)
if ((r1 == 0) and (r2 == 0) and (r3 == 0)):
print('Successfully identified human emotions!')
else:
print('Failed to identify the human emotions!')
var2 = datetime.datetime.now()
c = var2 var1
minutes = c.total_seconds() / 60
print('Total difference in minutes: ', str(minutes))
print('End Time: ', str(var1))
except Exception as e:
x = str(e)
print('Error: ', x)
if __name__ == "__main__":
main()

The key-snippet from the above script are as follows –

# Instantiating all the three classes

x1 = fed.clsFaceEmotionDetect()
x2 = fv.clsFrame2Video()
x3 = vp.clsVideoPlay()

As one can see from the above snippet, all the major classes are instantiated & loaded into the memory.

# Execute all the pass
r1 = x1.readEmotion(debugInd, var)
r2 = x2.convert2Vid(debugInd, var)
r3 = x3.stream(debugInd, var)

All the responses are captured into the corresponding variables, which later check for success status.


Let us capture & compare the emotions in a screenshot for better understanding –

Emotion Analysis

So, one can see that most of the frames from the video & above-posted frame correctly identify the human emotions.


FOLDER STRUCTURE:

Here is the folder structure that contains all the files & directories in MAC O/S –

Directory

So, we’ve done it.

You will get the complete codebase in the following Github link.

If you want to know more about this legendary director & his famous work, please visit the following link.

I’ll bring some more exciting topic in the coming days from the Python verse. Please share & subscribe my post & let me know your feedback.

Till then, Happy Avenging! 😀

Note: All the data & scenario posted here are representational data & scenarios & available over the internet & for educational purpose only. Some of the images (except my photo) that we’ve used are available over the net. We don’t claim the ownership of these images. There is an always room for improvement & especially the prediction quality.

Python-based dash framework visualizing real-time covid-19 trend.

Hi Team,

We’ll enhance our last post on Covid-19 prediction & try to capture them in a real-time dashboard, where the values in the visual display points will be affected as soon as the source data changes. In short, this is genuinely a real-time visual dashboard displaying all the graphs, trends depending upon the third-party API source data change.

However, I would like to share the run before we dig deep into this.

Demo Run

Architecture:

Let us understand the architecture for this solution –

Streaming Architecture

From the above diagram, one can see that we’re maintaining a similar approach compared to our last initiative. However, we’ve used a different framework to display the data live.

To achieve this, we’ve used a compelling python-based framework called Dash. Other than that, we’ve used Ably, Plotly & Prophet API.

If you need to know more about our last post, please visit this link.


Package Installation:

Let us understand the sample packages that require for this task.

Step – 1:

Installing Packages

Step – 2:

Installing Packages – Continue

Step – 3:

Installing Packages – Continue

Step – 4:

Installing Packages – Final

And, here is the command to install those packages –

pip install pandas
pip install plotly
pip install prophet
pip install dash
pip install pandas
pip install ably

Code:

Since this is an extension to our previous post, we’re not going to discuss other scripts, which we’ve already discussed over there. Instead, we will talk about the enhanced scripts & the new scripts that require for this use case.

1. clsConfig.py ( This native Python script contains the configuration entries. )


################################################
#### Written By: SATYAKI DE ####
#### Written On: 15-May-2020 ####
#### Modified On: 09-Sep-2021 ####
#### ####
#### Objective: This script is a config ####
#### file, contains all the keys for ####
#### Machine-Learning & streaming dashboard.####
#### ####
################################################
import os
import platform as pl
class clsConfig(object):
Curr_Path = os.path.dirname(os.path.realpath(__file__))
os_det = pl.system()
if os_det == "Windows":
sep = '\\'
else:
sep = '/'
conf = {
'APP_ID': 1,
'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
'LOG_PATH': Curr_Path + sep + 'log' + sep,
'REPORT_PATH': Curr_Path + sep + 'report',
'FILE_NAME': Curr_Path + sep + 'data' + sep + 'TradeIn.csv',
'SRC_PATH': Curr_Path + sep + 'data' + sep,
'APP_DESC_1': 'Dash Integration with Ably!',
'DEBUG_IND': 'N',
'INIT_PATH': Curr_Path,
'SUBDIR' : 'data',
'ABLY_ID': 'XXX2LL.93kdkiU2:Kdsldoeie737484E',
"URL":"https://corona-api.com/countries/",
"appType":"application/json",
"conType":"keep-alive",
"limRec": 10,
"CACHE":"no-cache",
"MAX_RETRY": 3,
"coList": "DE, IN, US, CA, GB, ID, BR",
"FNC": "NewConfirmed",
"TMS": "ReportedDate",
"FND": "NewDeaths",
"FinData": "Cache.csv"
}

view raw

clsConfig.py

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A few of the new entries, which are essential to this task are -> ABLY_ID & FinData.

2. clsPublishStream.py ( This script will publish the data transformed for Covid-19 predictions from the third-party sources. )


###############################################################
#### ####
#### Written By: Satyaki De ####
#### Written Date: 26-Jul-2021 ####
#### Modified Date: 08-Sep-2021 ####
#### ####
#### Objective: This script will publish real-time ####
#### streaming data coming out from a hosted API ####
#### sources using another popular third-party service ####
#### named Ably. Ably mimics pubsub Streaming concept, ####
#### which might be extremely useful for any start-ups. ####
#### ####
###############################################################
from ably import AblyRest
import logging
import json
from random import seed
from random import random
import json
import math
import random
from clsConfig import clsConfig as cf
# Global Section
logger = logging.getLogger('ably')
logger.addHandler(logging.StreamHandler())
ably_id = str(cf.conf['ABLY_ID'])
ably = AblyRest(ably_id)
channel = ably.channels.get('sd_channel')
# End Of Global Section
class clsPublishStream:
def __init__(self):
self.fnc = cf.conf['FNC']
def pushEvents(self, srcDF, debugInd, varVa, flg):
try:
# JSON data
# This is the default data for all the identified category
# we've prepared. You can extract this dynamically. Or, By
# default you can set their base trade details.
json_data = [{'Year_Mon': '201911', 'Brazil': 0.0, 'Canada': 0.0, 'Germany': 0.0, 'India': 0.0, 'Indonesia': 0.0, 'UnitedKingdom': 0.0, 'UnitedStates': 0.0, 'Status': flg},
{'Year_Mon': '201912', 'Brazil': 0.0, 'Canada': 0.0, 'Germany': 0.0, 'India': 0.0, 'Indonesia': 0.0, 'UnitedKingdom': 0.0, 'UnitedStates': 0.0, 'Status': flg}]
jdata = json.dumps(json_data)
# Publish a message to the sd_channel channel
channel.publish('event', jdata)
# Capturing the inbound dataframe
iDF = srcDF
# Adding new selected points
covid_dict = iDF.to_dict('records')
jdata_fin = json.dumps(covid_dict)
# Publish rest of the messages to the sd_channel channel
channel.publish('event', jdata_fin)
jdata_fin = ''
return 0
except Exception as e:
x = str(e)
print(x)
logging.info(x)
return 1

We’ve already discussed this script. The only new line that appears here is –

json_data = [{'Year_Mon': '201911', 'Brazil': 0.0, 'Canada': 0.0, 'Germany': 0.0, 'India': 0.0, 'Indonesia': 0.0, 'UnitedKingdom': 0.0, 'UnitedStates': 0.0, 'Status': flg},
            {'Year_Mon': '201912', 'Brazil': 0.0, 'Canada': 0.0, 'Germany': 0.0, 'India': 0.0, 'Indonesia': 0.0, 'UnitedKingdom': 0.0, 'UnitedStates': 0.0, 'Status': flg}]

This statement is more like a dummy feed, which creates the basic structure of your graph.

3. clsStreamConsume.py ( This script will consume the stream from Ably Queue configuration entries. )


##############################################
#### Written By: SATYAKI DE ####
#### Written On: 26-Jul-2021 ####
#### Modified On 08-Sep-2021 ####
#### ####
#### Objective: Consuming Streaming data ####
#### from Ably channels published by the ####
#### callPredictCovidAnalysisRealtime.py ####
#### ####
##############################################
import json
from clsConfig import clsConfig as cf
import requests
import logging
import time
import pandas as p
import clsL as cl
from ably import AblyRest
# Initiating Log class
l = cl.clsL()
class clsStreamConsume:
def __init__(self):
self.ably_id = str(cf.conf['ABLY_ID'])
self.fileName = str(cf.conf['FinData'])
def conStream(self, varVa, debugInd):
try:
ably_id = self.ably_id
fileName = self.fileName
var = varVa
debug_ind = debugInd
# Fetching the data
client = AblyRest(ably_id)
channel = client.channels.get('sd_channel')
message_page = channel.history()
# Counter Value
cnt = 0
# Declaring Global Data-Frame
df_conv = p.DataFrame()
for i in message_page.items:
print('Last Msg: {}'.format(i.data))
json_data = json.loads(i.data)
# Converting JSON to Dataframe
df = p.json_normalize(json_data)
df.columns = df.columns.map(lambda x: x.split(".")[1])
if cnt == 0:
df_conv = df
else:
d_frames = [df_conv, df]
df_conv = p.concat(d_frames)
cnt += 1
# Resetting the Index Value
df_conv.reset_index(drop=True, inplace=True)
# This will check whether the current load is happening
# or not. Based on that, it will capture the old events
# from cache.
if df_conv.empty:
df_conv = p.read_csv(fileName, index = True)
else:
l.logr(fileName, debug_ind, df_conv, 'log')
return df_conv
except Exception as e:
x = str(e)
print(x)
logging.info(x)
# This will handle the error scenaio as well.
# Based on that, it will capture the old events
# from cache.
try:
df_conv = p.read_csv(fileName, index = True)
except:
df = p.DataFrame()
return df

We’ve already discussed this script in one of my earlier posts, which you will get here.

So, I’m not going to discuss all the steps in detail.

The only added part was to introduce some temporary local caching mechanism.

if df_conv.empty:
    df_conv = p.read_csv(fileName, index = True)
else:
    l.logr(fileName, debug_ind, df_conv, 'log')

4. callPredictCovidAnalysisRealtime.py ( Main calling script to fetch the COVID-19 data from the third-party source & then publish it to the Ably message queue after transforming the data & adding the prediction using Facebook’s prophet API. )


##############################################
#### Written By: SATYAKI DE ####
#### Written On: 26-Jul-2021 ####
#### Modified On 26-Jul-2021 ####
#### ####
#### Objective: Calling multiple API's ####
#### that including Prophet-API developed ####
#### by Facebook for future prediction of ####
#### Covid-19 situations in upcoming days ####
#### for world's major hotspots. ####
##############################################
import json
import clsCovidAPI as ca
from clsConfig import clsConfig as cf
import datetime
import logging
import clsL as cl
import math as m
import clsPublishStream as cps
import clsForecast as f
from prophet import Prophet
from prophet.plot import plot_plotly, plot_components_plotly
import matplotlib.pyplot as plt
import pandas as p
import datetime as dt
import time
# Disbling Warning
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
# Initiating Log class
l = cl.clsL()
# Helper Function that removes underscores
def countryDet(inputCD):
try:
countryCD = inputCD
if str(countryCD) == 'DE':
cntCD = 'Germany'
elif str(countryCD) == 'BR':
cntCD = 'Brazil'
elif str(countryCD) == 'GB':
cntCD = 'UnitedKingdom'
elif str(countryCD) == 'US':
cntCD = 'UnitedStates'
elif str(countryCD) == 'IN':
cntCD = 'India'
elif str(countryCD) == 'CA':
cntCD = 'Canada'
elif str(countryCD) == 'ID':
cntCD = 'Indonesia'
else:
cntCD = 'N/A'
return cntCD
except:
cntCD = 'N/A'
return cntCD
def lookupCountry(row):
try:
strCD = str(row['CountryCode'])
retVal = countryDet(strCD)
return retVal
except:
retVal = 'N/A'
return retVal
def adjustTrend(row):
try:
flTrend = float(row['trend'])
flTrendUpr = float(row['trend_upper'])
flTrendLwr = float(row['trend_lower'])
retVal = m.trunc((flTrend + flTrendUpr + flTrendLwr)/3)
if retVal < 0:
retVal = 0
return retVal
except:
retVal = 0
return retVal
def ceilTrend(row, colName):
try:
flTrend = str(row[colName])
if flTrend.find('.'):
if float(flTrend) > 0:
retVal = m.trunc(float(flTrend)) + 1
else:
retVal = m.trunc(float(flTrend))
else:
retVal = float(flTrend)
if retVal < 0:
retVal = 0
return retVal
except:
retVal = 0
return retVal
def plot_picture(inputDF, debug_ind, var, countryCD, stat):
try:
iDF = inputDF
# Lowercase the column names
iDF.columns = [c.lower() for c in iDF.columns]
# Determine which is Y axis
y_col = [c for c in iDF.columns if c.startswith('y')][0]
# Determine which is X axis
x_col = [c for c in iDF.columns if c.startswith('ds')][0]
# Data Conversion
iDF['y'] = iDF[y_col].astype('float')
iDF['ds'] = iDF[x_col].astype('datetime64[ns]')
# Forecast calculations
# Decreasing the changepoint_prior_scale to 0.001 to make the trend less flexible
m = Prophet(n_changepoints=20, yearly_seasonality=True, changepoint_prior_scale=0.001)
#m = Prophet(n_changepoints=20, yearly_seasonality=True, changepoint_prior_scale=0.04525)
#m = Prophet(n_changepoints=['2021-09-10'])
m.fit(iDF)
forecastDF = m.make_future_dataframe(periods=365)
forecastDF = m.predict(forecastDF)
l.logr('15.forecastDF_' + var + '_' + countryCD + '.csv', debug_ind, forecastDF, 'log')
df_M = forecastDF[['ds', 'trend', 'trend_lower', 'trend_upper']]
l.logr('16.df_M_' + var + '_' + countryCD + '.csv', debug_ind, df_M, 'log')
# Getting Full Country Name
cntCD = countryDet(countryCD)
# Draw forecast results
df_M['Country'] = cntCD
l.logr('17.df_M_C_' + var + '_' + countryCD + '.csv', debug_ind, df_M, 'log')
df_M['AdjustTrend'] = df_M.apply(lambda row: adjustTrend(row), axis=1)
l.logr('20.df_M_AdjustTrend_' + var + '_' + countryCD + '.csv', debug_ind, df_M, 'log')
return df_M
except Exception as e:
x = str(e)
print(x)
df = p.DataFrame()
return df
def countrySpecificDF(counryDF, val):
try:
countryName = val
df = counryDF
df_lkpFile = df[(df['CountryCode'] == val)]
return df_lkpFile
except:
df = p.DataFrame()
return df
def toNum(row, colName):
try:
flTrend = str(row[colName])
flTr, subpart = flTrend.split(' ')
retVal = int(flTr.replace('-',''))
return retVal
except:
retVal = 0
return retVal
def extractPredictedDF(OrigDF, MergePredictedDF, colName):
try:
iDF_1 = OrigDF
iDF_2 = MergePredictedDF
dt_format = '%Y-%m-%d'
iDF_1_max_group = iDF_1.groupby(["Country"] , as_index=False)["ReportedDate"].max()
iDF_2['ReportedDate'] = iDF_2.apply(lambda row: toNum(row, 'ds'), axis=1)
col_one_list = iDF_1_max_group['Country'].tolist()
col_two_list = iDF_1_max_group['ReportedDate'].tolist()
print('col_one_list: ', str(col_one_list))
print('col_two_list: ', str(col_two_list))
cnt_1_x = 1
cnt_1_y = 1
cnt_x = 0
df_M = p.DataFrame()
for i in col_one_list:
str_countryVal = str(i)
cnt_1_y = 1
for j in col_two_list:
intReportDate = int(str(j).strip().replace('-',''))
if cnt_1_x == cnt_1_y:
print('str_countryVal: ', str(str_countryVal))
print('intReportDate: ', str(intReportDate))
iDF_2_M = iDF_2[(iDF_2['Country'] == str_countryVal) & (iDF_2['ReportedDate'] > intReportDate)]
# Merging with the previous Country Code data
if cnt_x == 0:
df_M = iDF_2_M
else:
d_frames = [df_M, iDF_2_M]
df_M = p.concat(d_frames)
cnt_x += 1
cnt_1_y += 1
cnt_1_x += 1
df_M.drop(columns=['ReportedDate'], axis=1, inplace=True)
df_M.rename(columns={'ds':'ReportedDate'}, inplace=True)
df_M.rename(columns={'AdjustTrend':colName}, inplace=True)
return df_M
except:
df = p.DataFrame()
return df
def toPivot(inDF, colName):
try:
iDF = inDF
iDF_Piv = iDF.pivot_table(colName, ['ReportedDate'], 'Country')
iDF_Piv.reset_index( drop=False, inplace=True )
list1 = ['ReportedDate']
iDF_Arr = iDF['Country'].unique()
list2 = iDF_Arr.tolist()
listV = list1 + list2
iDF_Piv.reindex([listV], axis=1)
return iDF_Piv
except Exception as e:
x = str(e)
print(x)
df = p.DataFrame()
return df
def toAgg(inDF, var, debugInd, flg):
try:
iDF = inDF
colName = "ReportedDate"
list1 = list(iDF.columns.values)
list1.remove(colName)
list1 = ["Brazil", "Canada", "Germany", "India", "Indonesia", "UnitedKingdom", "UnitedStates"]
iDF['Year_Mon'] = iDF[colName].apply(lambda x:x.strftime('%Y%m'))
iDF.drop(columns=[colName], axis=1, inplace=True)
ColNameGrp = "Year_Mon"
print('List1 Aggregate:: ', str(list1))
print('ColNameGrp :: ', str(ColNameGrp))
iDF_T = iDF[["Year_Mon", "Brazil", "Canada", "Germany", "India", "Indonesia", "UnitedKingdom", "UnitedStates"]]
iDF_T.fillna(0, inplace = True)
print('iDF_T:: ')
print(iDF_T)
iDF_1_max_group = iDF_T.groupby(ColNameGrp, as_index=False)[list1].sum()
iDF_1_max_group['Status'] = flg
return iDF_1_max_group
except Exception as e:
x = str(e)
print(x)
df = p.DataFrame()
return df
def publishEvents(inDF1, inDF2, inDF3, inDF4, var, debugInd):
try:
# Original Covid Data from API
iDF1 = inDF1
iDF2 = inDF2
NC = 'NewConfirmed'
ND = 'NewDeaths'
iDF1_PV = toPivot(iDF1, NC)
iDF1_PV['ReportedDate'] = p.to_datetime(iDF1_PV['ReportedDate'])
l.logr('57.iDF1_PV_' + var + '.csv', debugInd, iDF1_PV, 'log')
iDF2_PV = toPivot(iDF2, ND)
iDF2_PV['ReportedDate'] = p.to_datetime(iDF2_PV['ReportedDate'])
l.logr('58.iDF2_PV_' + var + '.csv', debugInd, iDF2_PV, 'log')
# Predicted Covid Data from Facebook API
iDF3 = inDF3
iDF4 = inDF4
iDF3_PV = toPivot(iDF3, NC)
l.logr('59.iDF3_PV_' + var + '.csv', debugInd, iDF3_PV, 'log')
iDF4_PV = toPivot(iDF4, ND)
l.logr('60.iDF4_PV_' + var + '.csv', debugInd, iDF4_PV, 'log')
# Now aggregating data based on year-month only
iDF1_Agg = toAgg(iDF1_PV, var, debugInd, NC)
l.logr('61.iDF1_Agg_' + var + '.csv', debugInd, iDF1_Agg, 'log')
iDF2_Agg = toAgg(iDF2_PV, var, debugInd, ND)
l.logr('62.iDF2_Agg_' + var + '.csv', debugInd, iDF2_Agg, 'log')
iDF3_Agg = toAgg(iDF3_PV, var, debugInd, NC)
l.logr('63.iDF3_Agg_' + var + '.csv', debugInd, iDF3_Agg, 'log')
iDF4_Agg = toAgg(iDF4_PV, var, debugInd, ND)
l.logr('64.iDF4_Agg_' + var + '.csv', debugInd, iDF4_Agg, 'log')
# Initiating Ably class to push events
x1 = cps.clsPublishStream()
# Pushing both the Historical Confirmed Cases
retVal_1 = x1.pushEvents(iDF1_Agg, debugInd, var, NC)
if retVal_1 == 0:
print('Successfully historical event pushed!')
else:
print('Failed to push historical events!')
# Pushing both the Historical Death Cases
retVal_3 = x1.pushEvents(iDF2_Agg, debugInd, var, ND)
if retVal_3 == 0:
print('Successfully historical event pushed!')
else:
print('Failed to push historical events!')
time.sleep(5)
# Pushing both the New Confirmed Cases
retVal_2 = x1.pushEvents(iDF3_Agg, debugInd, var, NC)
if retVal_2 == 0:
print('Successfully predicted event pushed!')
else:
print('Failed to push predicted events!')
# Pushing both the New Death Cases
retVal_4 = x1.pushEvents(iDF4_Agg, debugInd, var, ND)
if retVal_4 == 0:
print('Successfully predicted event pushed!')
else:
print('Failed to push predicted events!')
return 0
except Exception as e:
x = str(e)
print(x)
return 1
def main():
try:
var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
print('*' *60)
DInd = 'Y'
NC = 'New Confirmed'
ND = 'New Dead'
SM = 'data process Successful!'
FM = 'data process Failure!'
print("Calling the custom Package for large file splitting..")
print('Start Time: ' + str(var1))
countryList = str(cf.conf['coList']).split(',')
# Initiating Log Class
general_log_path = str(cf.conf['LOG_PATH'])
# Enabling Logging Info
logging.basicConfig(filename=general_log_path + 'CovidAPI.log', level=logging.INFO)
# Create the instance of the Covid API Class
x1 = ca.clsCovidAPI()
# Let's pass this to our map section
retDF = x1.searchQry(var1, DInd)
retVal = int(retDF.shape[0])
if retVal > 0:
print('Successfully Covid Data Extracted from the API-source.')
else:
print('Something wrong with your API-source!')
# Extracting Skeleton Data
df = retDF[['data.code', 'date', 'deaths', 'confirmed', 'recovered', 'new_confirmed', 'new_recovered', 'new_deaths', 'active']]
df.columns = ['CountryCode', 'ReportedDate', 'TotalReportedDead', 'TotalConfirmedCase', 'TotalRecovered', 'NewConfirmed', 'NewRecovered', 'NewDeaths', 'ActiveCaases']
df.dropna()
print('Returned Skeleton Data Frame: ')
print(df)
l.logr('5.df_' + var1 + '.csv', DInd, df, 'log')
# Due to source data issue, application will perform of
# avg of counts based on dates due to multiple entries
g_df = df.groupby(["CountryCode", "ReportedDate"] , as_index=False)["TotalReportedDead","TotalConfirmedCase","TotalRecovered","NewConfirmed","NewRecovered","NewDeaths","ActiveCaases"].mean()
g_df['TotalReportedDead_M'] = g_df.apply(lambda row: ceilTrend(row, 'TotalReportedDead'), axis=1)
g_df['TotalConfirmedCase_M'] = g_df.apply(lambda row: ceilTrend(row, 'TotalConfirmedCase'), axis=1)
g_df['TotalRecovered_M'] = g_df.apply(lambda row: ceilTrend(row, 'TotalRecovered'), axis=1)
g_df['NewConfirmed_M'] = g_df.apply(lambda row: ceilTrend(row, 'NewConfirmed'), axis=1)
g_df['NewRecovered_M'] = g_df.apply(lambda row: ceilTrend(row, 'NewRecovered'), axis=1)
g_df['NewDeaths_M'] = g_df.apply(lambda row: ceilTrend(row, 'NewDeaths'), axis=1)
g_df['ActiveCaases_M'] = g_df.apply(lambda row: ceilTrend(row, 'ActiveCaases'), axis=1)
# Dropping old columns
g_df.drop(columns=['TotalReportedDead', 'TotalConfirmedCase', 'TotalRecovered', 'NewConfirmed', 'NewRecovered', 'NewDeaths', 'ActiveCaases'], axis=1, inplace=True)
# Renaming the new columns to old columns
g_df.rename(columns={'TotalReportedDead_M':'TotalReportedDead'}, inplace=True)
g_df.rename(columns={'TotalConfirmedCase_M':'TotalConfirmedCase'}, inplace=True)
g_df.rename(columns={'TotalRecovered_M':'TotalRecovered'}, inplace=True)
g_df.rename(columns={'NewConfirmed_M':'NewConfirmed'}, inplace=True)
g_df.rename(columns={'NewRecovered_M':'NewRecovered'}, inplace=True)
g_df.rename(columns={'NewDeaths_M':'NewDeaths'}, inplace=True)
g_df.rename(columns={'ActiveCaases_M':'ActiveCaases'}, inplace=True)
l.logr('5.g_df_' + var1 + '.csv', DInd, g_df, 'log')
# Working with forecast
# Create the instance of the Forecast API Class
x2 = f.clsForecast()
# Fetching each country name & then get the details
cnt = 6
cnt_x = 0
cnt_y = 0
df_M_Confirmed = p.DataFrame()
df_M_Deaths = p.DataFrame()
for i in countryList:
try:
cntryIndiv = i.strip()
cntryFullName = countryDet(cntryIndiv)
print('Country Porcessing: ' + str(cntryFullName))
# Creating dataframe for each country
# Germany Main DataFrame
dfCountry = countrySpecificDF(g_df, cntryIndiv)
l.logr(str(cnt) + '.df_' + cntryIndiv + '_' + var1 + '.csv', DInd, dfCountry, 'log')
# Let's pass this to our map section
retDFGenNC = x2.forecastNewConfirmed(dfCountry, DInd, var1)
statVal = str(NC)
a1 = plot_picture(retDFGenNC, DInd, var1, cntryIndiv, statVal)
# Merging with the previous Country Code data
if cnt_x == 0:
df_M_Confirmed = a1
else:
d_frames = [df_M_Confirmed, a1]
df_M_Confirmed = p.concat(d_frames)
cnt_x += 1
retDFGenNC_D = x2.forecastNewDead(dfCountry, DInd, var1)
statVal = str(ND)
a2 = plot_picture(retDFGenNC_D, DInd, var1, cntryIndiv, statVal)
# Merging with the previous Country Code data
if cnt_y == 0:
df_M_Deaths = a2
else:
d_frames = [df_M_Deaths, a2]
df_M_Deaths = p.concat(d_frames)
cnt_y += 1
# Printing Proper message
if (a1 + a2) == 0:
oprMsg = cntryFullName + ' ' + SM
print(oprMsg)
else:
oprMsg = cntryFullName + ' ' + FM
print(oprMsg)
# Resetting the dataframe value for the next iteration
dfCountry = p.DataFrame()
cntryIndiv = ''
oprMsg = ''
cntryFullName = ''
a1 = 0
a2 = 0
statVal = ''
cnt += 1
except Exception as e:
x = str(e)
print(x)
l.logr('49.df_M_Confirmed_' + var1 + '.csv', DInd, df_M_Confirmed, 'log')
l.logr('50.df_M_Deaths_' + var1 + '.csv', DInd, df_M_Deaths, 'log')
# Removing unwanted columns
df_M_Confirmed.drop(columns=['trend', 'trend_lower', 'trend_upper'], axis=1, inplace=True)
df_M_Deaths.drop(columns=['trend', 'trend_lower', 'trend_upper'], axis=1, inplace=True)
l.logr('51.df_M_Confirmed_' + var1 + '.csv', DInd, df_M_Confirmed, 'log')
l.logr('52.df_M_Deaths_' + var1 + '.csv', DInd, df_M_Deaths, 'log')
# Creating original dataframe from the source API
df_M_Confirmed_Orig = g_df[['CountryCode', 'ReportedDate','NewConfirmed']]
df_M_Deaths_Orig = g_df[['CountryCode', 'ReportedDate','NewDeaths']]
# Transforming Country Code
df_M_Confirmed_Orig['Country'] = df_M_Confirmed_Orig.apply(lambda row: lookupCountry(row), axis=1)
df_M_Deaths_Orig['Country'] = df_M_Deaths_Orig.apply(lambda row: lookupCountry(row), axis=1)
# Dropping unwanted column
df_M_Confirmed_Orig.drop(columns=['CountryCode'], axis=1, inplace=True)
df_M_Deaths_Orig.drop(columns=['CountryCode'], axis=1, inplace=True)
# Reordering columns
df_M_Confirmed_Orig = df_M_Confirmed_Orig.reindex(['ReportedDate','Country','NewConfirmed'], axis=1)
df_M_Deaths_Orig = df_M_Deaths_Orig.reindex(['ReportedDate','Country','NewDeaths'], axis=1)
l.logr('53.df_M_Confirmed_Orig_' + var1 + '.csv', DInd, df_M_Confirmed_Orig, 'log')
l.logr('54.df_M_Deaths_Orig_' + var1 + '.csv', DInd, df_M_Deaths_Orig, 'log')
# Filter out only the predicted data
filterDF_1 = extractPredictedDF(df_M_Confirmed_Orig, df_M_Confirmed, 'NewConfirmed')
l.logr('55.filterDF_1_' + var1 + '.csv', DInd, filterDF_1, 'log')
filterDF_2 = extractPredictedDF(df_M_Confirmed_Orig, df_M_Confirmed, 'NewDeaths')
l.logr('56.filterDF_2_' + var1 + '.csv', DInd, filterDF_2, 'log')
# Calling the final publish events
retVa = publishEvents(df_M_Confirmed_Orig, df_M_Deaths_Orig, filterDF_1, filterDF_2, var1, DInd)
if retVa == 0:
print('Successfully stream processed!')
else:
print('Failed to process stream!')
var2 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
print('End Time: ' + str(var2))
print('*' *60)
except Exception as e:
x = str(e)
print(x)
if __name__ == "__main__":
main()

Let us understand the enhancement part of this script –

We’ve taken out the plotly part as we will use a separate dashboard script to visualize the data trend.

However, we need to understand the initial consumed data from API & how we transform the data, which will be helpful for visualization.

The initial captured data should look like this after extracting only the relevant elements from the API response.

Initial Data from API

As you can see that based on the country & reported date, our application is consuming attributes like Total-Reported-Death, Total-Recovered, New-death, New-Confirmed & so on.

From this list, we’ve taken two attributes for our use cases & they are New-Death & New-Confirmed. Also, we’re predicting the Future-New-Death & Future-New-Confirmed based on the historical data using Facebook’s prophet API.

And, we would be transposing them & extract the countries & put them as columns for better representations.

Transposed Data

Hence, here is the code that we should be exploring –

def toPivot(inDF, colName):
    try:
        iDF = inDF

        iDF_Piv = iDF.pivot_table(colName, ['ReportedDate'], 'Country')
        iDF_Piv.reset_index( drop=False, inplace=True )

        list1 = ['ReportedDate']

        iDF_Arr = iDF['Country'].unique()
        list2 = iDF_Arr.tolist()

        listV = list1 + list2

        iDF_Piv.reindex([listV], axis=1)

        return iDF_Piv
    except Exception as e:
        x = str(e)
        print(x)

        df = p.DataFrame()

        return df

Now, using the pivot_table function, we’re transposing the row values into the columns. And, later we’ve realigned the column heading as per our desired format.

However, we still have the data as per individual daily dates in this case. We want to eliminate that by removing the daypart & then aggregate them by month as shown below –

Aggregated Data

And, here is the code for that –

def toAgg(inDF, var, debugInd, flg):
    try:
        iDF = inDF
        colName = "ReportedDate"

        list1 = list(iDF.columns.values)
        list1.remove(colName)

        list1 = ["Brazil", "Canada", "Germany", "India", "Indonesia", "UnitedKingdom", "UnitedStates"]

        iDF['Year_Mon'] = iDF[colName].apply(lambda x:x.strftime('%Y%m'))
        iDF.drop(columns=[colName], axis=1, inplace=True)

        ColNameGrp = "Year_Mon"
        print('List1 Aggregate:: ', str(list1))
        print('ColNameGrp :: ', str(ColNameGrp))

        iDF_T = iDF[["Year_Mon", "Brazil", "Canada", "Germany", "India", "Indonesia", "UnitedKingdom", "UnitedStates"]]
        iDF_T.fillna(0, inplace = True)
        print('iDF_T:: ')
        print(iDF_T)

        iDF_1_max_group = iDF_T.groupby(ColNameGrp, as_index=False)[list1].sum()
        iDF_1_max_group['Status'] = flg

        return iDF_1_max_group
    except Exception as e:
        x = str(e)
        print(x)

        df = p.DataFrame()

        return df

From the above snippet we can conclude that the application is taking out the daypart & then aggregate it based on the Year_Mon attribute.

The following snippet will push the final transformed data to Ably queue –

x1 = cps.clsPublishStream()

# Pushing both the Historical Confirmed Cases
retVal_1 = x1.pushEvents(iDF1_Agg, debugInd, var, NC)

if retVal_1 == 0:
    print('Successfully historical event pushed!')
else:
    print('Failed to push historical events!')

5. dashboard_realtime.py ( Main calling script to consume the data from Ably queue & then visualize the trend. )


##############################################
#### Written By: SATYAKI DE ####
#### Written On: 08-Sep-2021 ####
#### Modified On 08-Sep-2021 ####
#### ####
#### Objective: This is the main script ####
#### to invoke dashboard after consuming ####
#### streaming real-time predicted data ####
#### using Facebook API & Ably message Q. ####
#### ####
#### This script will show the trend ####
#### comparison between major democracies ####
#### of the world. ####
#### ####
##############################################
import datetime
import dash
from dash import dcc
from dash import html
import plotly
from dash.dependencies import Input, Output
from ably import AblyRest
from clsConfig import clsConfig as cf
import pandas as p
# Main Class to consume streaming
import clsStreamConsume as ca
import numpy as np
# Create the instance of the Covid API Class
x1 = ca.clsStreamConsume()
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css&#39;]
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(
html.Div([
html.H1("Covid-19 Trend Dashboard",
className='text-center text-primary mb-4'),
html.H5(children='''
Dash: Covid-19 Trend – (Present Vs Future)
'''),
html.P("Covid-19: New Confirmed Cases:",
style={"textDecoration": "underline"}),
dcc.Graph(id='live-update-graph-1'),
html.P("Covid-19: New Death Cases:",
style={"textDecoration": "underline"}),
dcc.Graph(id='live-update-graph-2'),
dcc.Interval(
id='interval-component',
interval=5*1000, # in milliseconds
n_intervals=0
)
], className="row", style={'marginBottom': 10, 'marginTop': 10})
)
def to_OptimizeString(row):
try:
x_str = str(row['Year_Mon'])
dt_format = '%Y%m%d'
finStr = x_str + '01'
strReportDate = datetime.datetime.strptime(finStr, dt_format)
return strReportDate
except Exception as e:
x = str(e)
print(x)
dt_format = '%Y%m%d'
var = '20990101'
strReportDate = datetime.strptime(var, dt_format)
return strReportDate
def fetchEvent(var1, DInd):
try:
# Let's pass this to our map section
iDF_M = x1.conStream(var1, DInd)
# Converting Year_Mon to dates
iDF_M['Year_Mon_Mod']= iDF_M.apply(lambda row: to_OptimizeString(row), axis=1)
# Dropping old columns
iDF_M.drop(columns=['Year_Mon'], axis=1, inplace=True)
#Renaming new column to old column
iDF_M.rename(columns={'Year_Mon_Mod':'Year_Mon'}, inplace=True)
return iDF_M
except Exception as e:
x = str(e)
print(x)
iDF_M = p.DataFrame()
return iDF_M
# Multiple components can update everytime interval gets fired.
@app.callback(Output('live-update-graph-1', 'figure'),
Input('interval-component', 'n_intervals'))
def update_graph_live(n):
try:
var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
print('*' *60)
DInd = 'Y'
# Let's pass this to our map section
retDF = fetchEvent(var1, DInd)
# Create the graph with subplots
#fig = plotly.tools.make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.3, horizontal_spacing=0.2)
fig = plotly.tools.make_subplots(rows=2, cols=1, vertical_spacing=0.3, horizontal_spacing=0.2)
# Routing data to dedicated DataFrame
retDFNC = retDF.loc[(retDF['Status'] == 'NewConfirmed')]
# Adding different chart into one dashboard
# First Use Case – New Confirmed
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Brazil,'type':'scatter','name':'Brazil'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Canada,'type':'scatter','name':'Canada'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Germany,'type':'scatter','name':'Germany'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.India,'type':'scatter','name':'India'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Indonesia,'type':'scatter','name':'Indonesia'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.UnitedKingdom,'type':'scatter','name':'United Kingdom'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.UnitedStates,'type':'scatter','name':'United States'},1,1)
return fig
except Exception as e:
x = str(e)
print(x)
# Create the graph with subplots
fig = plotly.tools.make_subplots(rows=2, cols=1, vertical_spacing=0.2)
fig['layout']['margin'] = {
'l': 30, 'r': 10, 'b': 30, 't': 10
}
fig['layout']['legend'] = {'x': 0, 'y': 1, 'xanchor': 'left'}
return fig
# Multiple components can update everytime interval gets fired.
@app.callback(Output('live-update-graph-2', 'figure'),
Input('interval-component', 'n_intervals'))
def update_graph_live(n):
try:
var1 = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
print('*' *60)
DInd = 'Y'
# Let's pass this to our map section
retDF = fetchEvent(var1, DInd)
# Create the graph with subplots
#fig = plotly.tools.make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.3, horizontal_spacing=0.2)
fig = plotly.tools.make_subplots(rows=2, cols=1, vertical_spacing=0.3, horizontal_spacing=0.2)
# Routing data to dedicated DataFrame
retDFND = retDF.loc[(retDF['Status'] == 'NewDeaths')]
# Adding different chart into one dashboard
# Second Use Case – New Confirmed
fig.append_trace({'x':retDFND.Year_Mon,'y':retDFND.Brazil,'type':'bar','name':'Brazil'},1,1)
fig.append_trace({'x':retDFND.Year_Mon,'y':retDFND.Canada,'type':'bar','name':'Canada'},1,1)
fig.append_trace({'x':retDFND.Year_Mon,'y':retDFND.Germany,'type':'bar','name':'Germany'},1,1)
fig.append_trace({'x':retDFND.Year_Mon,'y':retDFND.India,'type':'bar','name':'India'},1,1)
fig.append_trace({'x':retDFND.Year_Mon,'y':retDFND.Indonesia,'type':'bar','name':'Indonesia'},1,1)
fig.append_trace({'x':retDFND.Year_Mon,'y':retDFND.UnitedKingdom,'type':'bar','name':'United Kingdom'},1,1)
fig.append_trace({'x':retDFND.Year_Mon,'y':retDFND.UnitedStates,'type':'bar','name':'United States'},1,1)
return fig
except Exception as e:
x = str(e)
print(x)
# Create the graph with subplots
fig = plotly.tools.make_subplots(rows=2, cols=1, vertical_spacing=0.2)
fig['layout']['margin'] = {
'l': 30, 'r': 10, 'b': 30, 't': 10
}
fig['layout']['legend'] = {'x': 0, 'y': 1, 'xanchor': 'left'}
return fig
if __name__ == '__main__':
app.run_server(debug=True)

Let us explore the critical snippet as this is a brand new script –

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(
    html.Div([
        html.H1("Covid-19 Trend Dashboard",
                        className='text-center text-primary mb-4'),
        html.H5(children='''
            Dash: Covid-19 Trend - (Present Vs Future)
        '''),
        html.P("Covid-19: New Confirmed Cases:",
               style={"textDecoration": "underline"}),
        dcc.Graph(id='live-update-graph-1'),
        html.P("Covid-19: New Death Cases:",
               style={"textDecoration": "underline"}),
        dcc.Graph(id='live-update-graph-2'),
        dcc.Interval(
            id='interval-component',
            interval=5*1000, # in milliseconds
            n_intervals=0
        )
    ], className="row", style={'marginBottom': 10, 'marginTop': 10})
)

You need to understand the basics of HTML as this framework works seamlessly with it. To know more about the supported HTML, one needs to visit the following link.

def to_OptimizeString(row):
    try:
        x_str = str(row['Year_Mon'])

        dt_format = '%Y%m%d'
        finStr = x_str + '01'

        strReportDate = datetime.datetime.strptime(finStr, dt_format)

        return strReportDate

    except Exception as e:
        x = str(e)
        print(x)

        dt_format = '%Y%m%d'
        var = '20990101'

        strReportDate = datetime.strptime(var, dt_format)

        return strReportDate

The application is converting Year-Month combinations from string to date for better projection.

Also, we’ve implemented a dashboard that will refresh every five milliseconds.

def fetchEvent(var1, DInd):
    try:
        # Let's pass this to our map section
        iDF_M = x1.conStream(var1, DInd)

        # Converting Year_Mon to dates
        iDF_M['Year_Mon_Mod']= iDF_M.apply(lambda row: to_OptimizeString(row), axis=1)

        # Dropping old columns
        iDF_M.drop(columns=['Year_Mon'], axis=1, inplace=True)

        #Renaming new column to old column
        iDF_M.rename(columns={'Year_Mon_Mod':'Year_Mon'}, inplace=True)

        return iDF_M

    except Exception as e:
        x = str(e)
        print(x)

        iDF_M = p.DataFrame()

        return iDF_M

The application will consume all the events from the Ably Queue using the above snippet.

@app.callback(Output('live-update-graph-1', 'figure'),
              Input('interval-component', 'n_intervals'))
def update_graph_live(n):

We’ve implemented the callback mechanism to get the latest data from the Queue & then update the graph accordingly & finally share the updated chart & return that to our method, which is calling it.

# Routing data to dedicated DataFrame
retDFNC = retDF.loc[(retDF['Status'] == 'NewConfirmed')]

Based on the flag, we’re pushing the data into our target dataframe, from where the application will consume the data into the charts.

fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Brazil,'type':'scatter','name':'Brazil'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Canada,'type':'scatter','name':'Canada'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Germany,'type':'scatter','name':'Germany'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.India,'type':'scatter','name':'India'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.Indonesia,'type':'scatter','name':'Indonesia'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.UnitedKingdom,'type':'scatter','name':'United Kingdom'},1,1)
fig.append_trace({'x':retDFNC.Year_Mon,'y':retDFNC.UnitedStates,'type':'scatter','name':'United States'},1,1)

Different country’s KPI elements are fetched & mapped into their corresponding axis to project the graph with visual details.

Same approach goes for the other graph as well.


Run:

Let us run the application –

Run – Beginning
Run – Finishing Stage

Dashboard:

Dashboard Job Run
Dashboard Visualization

So, we’ve done it.

You will get the complete codebase in the following Github link.

I’ll bring some more exciting topic in the coming days from the Python verse.

Till then, Happy Avenging! 😀


Note: All the data & scenario posted here are representational data & scenarios & available over the internet & for educational purpose only.

One more thing you need to understand is that this prediction based on limited data points. The actual event may happen differently. Ideally, countries are taking a cue from this kind of analysis & are initiating appropriate measures to avoid the high-curve. And, that is one of the main objective of time series analysis.

There is always a room for improvement of this kind of models & the solution associated with it. I’ve shown the basic ways to achieve the same for the education purpose only.

Canada’s Covid19 analysis based on Logistic Regression

Hi Guys,

Today, I’ll be demonstrating some scenarios based on open-source data from Canada. In this post, I will only explain some of the significant parts of the code. Not the entire range of scripts here.

Let’s explore a couple of sample source data –

2. Sample Input Data

I would like to explore how much this disease caused an impact on the elderly in Canada.

Let’s explore the source directory structure –

3. Source Directory Structures

For this, you need to install the following packages –

pip install pandas

pip install seaborn

Please find the PyPi link given below –

In this case, we’ve downloaded the data from Canada’s site. However, they have created API. So, you can consume the data through that way as well. Since the volume is a little large. I decided to download that in CSV & then use that for my analysis.

Before I start, let me explain a couple of critical assumptions that I had to make due to data impurities or availabilities.

  • If there is no data available for a specific case, my application will consider that patient as COVID-Active.
  • We will consider the patient is affected through Community-spreading until we have data to find it otherwise.
  • If there is no data available for gender, we’re marking these records as “Other.” So, that way, we’re making it into that category, where the patient doesn’t want to disclose their sexual orientation.
  • If we don’t have any data, then by default, the application is considering the patient is alive.
  • Lastly, my application considers the middle point of the age range data for all the categories, i.e., the patient’s age between 20 & 30 will be considered as 25.

1. clsCovidAnalysisByCountryAdv (This is the main script, which will invoke the Machine-Learning API & return 0 if successful.)

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 01-Jun-2020              ####
#### Modified On 01-Jun-2020              ####
####                                      ####
#### Objective: Main scripts for Logistic ####
#### Regression.                          ####
##############################################

import pandas as p
import clsL as log
import datetime

import matplotlib.pyplot as plt
import seaborn as sns
from clsConfig import clsConfig as cf

# %matplotlib inline -- for Jupyter Notebook
class clsCovidAnalysisByCountryAdv:
    def __init__(self):
        self.fileName_1 = cf.config['FILE_NAME_1']
        self.fileName_2 = cf.config['FILE_NAME_2']
        self.Ind = cf.config['DEBUG_IND']
        self.subdir = str(cf.config['LOG_DIR_NAME'])

    def setDefaultActiveCases(self, row):
        try:
            str_status = str(row['case_status'])

            if str_status == 'Not Reported':
                return 'Active'
            else:
                return str_status
        except:
            return 'Active'

    def setDefaultExposure(self, row):
        try:
            str_exposure = str(row['exposure'])

            if str_exposure == 'Not Reported':
                return 'Community'
            else:
                return str_exposure
        except:
            return 'Community'

    def setGender(self, row):
        try:
            str_gender = str(row['gender'])

            if str_gender == 'Not Reported':
                return 'Other'
            else:
                return str_gender
        except:
            return 'Other'

    def setSurviveStatus(self, row):
        try:
            # 0 - Deceased
            # 1 - Alive
            str_active = str(row['ActiveCases'])

            if str_active == 'Deceased':
                return 0
            else:
                return 1
        except:
            return 1

    def getAgeFromGroup(self, row):
        try:
            # We'll take the middle of the Age group
            # If a age range falls with 20, we'll
            # consider this as 10.
            # Similarly, a age group between 20 & 30,
            # should reflect by 25.
            # Anything above 80 will be considered as
            # 85

            str_age_group = str(row['AgeGroup'])

            if str_age_group == '<20':
                return 10
            elif str_age_group == '20-29':
                return 25
            elif str_age_group == '30-39':
                return 35
            elif str_age_group == '40-49':
                return 45
            elif str_age_group == '50-59':
                return 55
            elif str_age_group == '60-69':
                return 65
            elif str_age_group == '70-79':
                return 75
            else:
                return 85
        except:
            return 100

    def predictResult(self):
        try:
            
            # Initiating Logging Instances
            clog = log.clsL()

            # Important variables
            var = datetime.datetime.now().strftime(".%H.%M.%S")
            print('Target File Extension will contain the following:: ', var)
            Ind = self.Ind
            subdir = self.subdir

            #######################################
            #                                     #
            # Using Logistic Regression to        #
            # Idenitfy the following scenarios -  #
            #                                     #
            # Age wise Infection Vs Deaths        #
            #                                     #
            #######################################
            inputFileName_2 = self.fileName_2

            # Reading from Input File
            df_2 = p.read_csv(inputFileName_2)

            # Fetching only relevant columns
            df_2_Mod = df_2[['date_reported','age_group','gender','exposure','case_status']]
            df_2_Mod['State'] = df_2['province_abbr']

            print()
            print('Projecting 2nd file sample rows: ')
            print(df_2_Mod.head())

            print()
            x_row_1 = df_2_Mod.shape[0]
            x_col_1 = df_2_Mod.shape[1]

            print('Total Number of Rows: ', x_row_1)
            print('Total Number of columns: ', x_col_1)

            #########################################################################################
            # Few Assumptions                                                                       #
            #########################################################################################
            # By default, if there is no data on exposure - We'll treat that as community spreading #
            # By default, if there is no data on case_status - We'll consider this as active        #
            # By default, if there is no data on gender - We'll put that under a separate Gender    #
            # category marked as the "Other". This includes someone who doesn't want to identify    #
            # his/her gender or wants to be part of LGBT community in a generic term.               #
            #                                                                                       #
            # We'll transform our data accordingly based on the above logic.                        #
            #########################################################################################
            df_2_Mod['ActiveCases'] = df_2_Mod.apply(lambda row: self.setDefaultActiveCases(row), axis=1)
            df_2_Mod['ExposureStatus'] = df_2_Mod.apply(lambda row: self.setDefaultExposure(row), axis=1)
            df_2_Mod['Gender'] = df_2_Mod.apply(lambda row: self.setGender(row), axis=1)

            # Filtering all other records where we don't get any relevant information
            # Fetching Data for
            df_3 = df_2_Mod[(df_2_Mod['age_group'] != 'Not Reported')]

            # Dropping unwanted columns
            df_3.drop(columns=['exposure'], inplace=True)
            df_3.drop(columns=['case_status'], inplace=True)
            df_3.drop(columns=['date_reported'], inplace=True)
            df_3.drop(columns=['gender'], inplace=True)

            # Renaming one existing column
            df_3.rename(columns={"age_group": "AgeGroup"}, inplace=True)

            # Creating important feature
            # 0 - Deceased
            # 1 - Alive
            df_3['Survived'] = df_3.apply(lambda row: self.setSurviveStatus(row), axis=1)

            clog.logr('2.df_3' + var + '.csv', Ind, df_3, subdir)

            print()
            print('Projecting Filter sample rows: ')
            print(df_3.head())

            print()
            x_row_2 = df_3.shape[0]
            x_col_2 = df_3.shape[1]

            print('Total Number of Rows: ', x_row_2)
            print('Total Number of columns: ', x_col_2)

            # Let's do some basic checkings
            sns.set_style('whitegrid')
            #sns.countplot(x='Survived', hue='Gender', data=df_3, palette='RdBu_r')

            # Fixing Gender Column
            # This will check & indicate yellow for missing entries
            #sns.heatmap(df_3.isnull(), yticklabels=False, cbar=False, cmap='viridis')

            #sex = p.get_dummies(df_3['Gender'], drop_first=True)
            sex = p.get_dummies(df_3['Gender'])
            df_4 = p.concat([df_3, sex], axis=1)

            print('After New addition of columns: ')
            print(df_4.head())

            clog.logr('3.df_4' + var + '.csv', Ind, df_4, subdir)

            # Dropping unwanted columns for our Machine Learning
            df_4.drop(columns=['Gender'], inplace=True)
            df_4.drop(columns=['ActiveCases'], inplace=True)
            df_4.drop(columns=['Male','Other','Transgender'], inplace=True)

            clog.logr('4.df_4_Mod' + var + '.csv', Ind, df_4, subdir)

            # Fixing Spread Columns
            spread = p.get_dummies(df_4['ExposureStatus'], drop_first=True)
            df_5 = p.concat([df_4, spread], axis=1)

            print('After Spread columns:')
            print(df_5.head())

            clog.logr('5.df_5' + var + '.csv', Ind, df_5, subdir)

            # Dropping unwanted columns for our Machine Learning
            df_5.drop(columns=['ExposureStatus'], inplace=True)

            clog.logr('6.df_5_Mod' + var + '.csv', Ind, df_5, subdir)

            # Fixing Age Columns
            df_5['Age'] = df_5.apply(lambda row: self.getAgeFromGroup(row), axis=1)
            df_5.drop(columns=["AgeGroup"], inplace=True)

            clog.logr('7.df_6' + var + '.csv', Ind, df_5, subdir)

            # Fixing Dummy Columns Name
            # Renaming one existing column Travel-Related with Travel_Related
            df_5.rename(columns={"Travel-Related": "TravelRelated"}, inplace=True)

            clog.logr('8.df_7' + var + '.csv', Ind, df_5, subdir)

            # Removing state for temporary basis
            df_5.drop(columns=['State'], inplace=True)
            # df_5.drop(columns=['State','Other','Transgender','Pending','TravelRelated','Male'], inplace=True)

            # Casting this entire dataframe into Integer
            # df_5_temp.apply(p.to_numeric)

            print('Info::')
            print(df_5.info())
            print("*" * 60)
            print(df_5.describe())
            print("*" * 60)

            clog.logr('9.df_8' + var + '.csv', Ind, df_5, subdir)

            print('Intermediate Sample Dataframe for Age::')
            print(df_5.head())

            # Plotting it to Graph
            sns.jointplot(x="Age", y='Survived', data=df_5)
            sns.jointplot(x="Age", y='Survived', data=df_5, kind='kde', color='red')
            plt.xlabel("Age")
            plt.ylabel("Data Point (0 - Died   Vs    1 - Alive)")

            # Another check with Age Group
            sns.countplot(x='Survived', hue='Age', data=df_5, palette='RdBu_r')
            plt.xlabel("Survived(0 - Died   Vs    1 - Alive)")
            plt.ylabel("Total No Of Patient")

            df_6 = df_5.drop(columns=['Survived'], axis=1)

            clog.logr('10.df_9' + var + '.csv', Ind, df_6, subdir)

            # Train & Split Data
            x_1 = df_6
            y_1 = df_5['Survived']

            # Now Train-Test Split of your source data
            from sklearn.model_selection import train_test_split

            # test_size => % of allocated data for your test cases
            # random_state => A specific set of random split on your data
            X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(x_1, y_1, test_size=0.3, random_state=101)

            # Importing Model
            from sklearn.linear_model import LogisticRegression

            logmodel = LogisticRegression()
            logmodel.fit(X_train_1, Y_train_1)

            # Adding Predictions to it
            predictions_1 = logmodel.predict(X_test_1)

            from sklearn.metrics import classification_report

            print('Classification Report:: ')
            print(classification_report(Y_test_1, predictions_1))

            from sklearn.metrics import confusion_matrix

            print('Confusion Matrix:: ')
            print(confusion_matrix(Y_test_1, predictions_1))

            # This is require when you are trying to print from conventional
            # front & not using Jupyter notebook.
            plt.show()

            return 0

        except Exception  as e:
            x = str(e)
            print('Error : ', x)

            return 1

Key snippets from the above script –

df_2_Mod['ActiveCases'] = df_2_Mod.apply(lambda row: self.setDefaultActiveCases(row), axis=1)
df_2_Mod['ExposureStatus'] = df_2_Mod.apply(lambda row: self.setDefaultExposure(row), axis=1)
df_2_Mod['Gender'] = df_2_Mod.apply(lambda row: self.setGender(row), axis=1)

# Filtering all other records where we don't get any relevant information
# Fetching Data for
df_3 = df_2_Mod[(df_2_Mod['age_group'] != 'Not Reported')]

# Dropping unwanted columns
df_3.drop(columns=['exposure'], inplace=True)
df_3.drop(columns=['case_status'], inplace=True)
df_3.drop(columns=['date_reported'], inplace=True)
df_3.drop(columns=['gender'], inplace=True)

# Renaming one existing column
df_3.rename(columns={"age_group": "AgeGroup"}, inplace=True)

# Creating important feature
# 0 - Deceased
# 1 - Alive
df_3['Survived'] = df_3.apply(lambda row: self.setSurviveStatus(row), axis=1)

The above lines point to the critical transformation areas, where the application is invoking various essential business logic.

Let’s see at this moment our sample data –

6. 4_4_mod

Let’s look into the following part –

# Fixing Spread Columns
spread = p.get_dummies(df_4['ExposureStatus'], drop_first=True)
df_5 = p.concat([df_4, spread], axis=1)

The above lines will transform the data into this –

7. 5_5_Mod

As you can see, we’ve transformed the row values into columns with binary values. This kind of transformation is beneficial.

# Plotting it to Graph
sns.jointplot(x="Age", y='Survived', data=df_5)
sns.jointplot(x="Age", y='Survived', data=df_5, kind='kde', color='red')
plt.xlabel("Age")
plt.ylabel("Data Point (0 - Died   Vs    1 - Alive)")

# Another check with Age Group
sns.countplot(x='Survived', hue='Age', data=df_5, palette='RdBu_r')
plt.xlabel("Survived(0 - Died   Vs    1 - Alive)")
plt.ylabel("Total No Of Patient")

The above lines will process the data & visualize based on that.

x_1 = df_6
y_1 = df_5['Survived']

In the above snippet, we’ve assigned the features & target variable for our final logistic regression model.

# Now Train-Test Split of your source data
from sklearn.model_selection import train_test_split

# test_size => % of allocated data for your test cases
# random_state => A specific set of random split on your data
X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(x_1, y_1, test_size=0.3, random_state=101)

# Importing Model
from sklearn.linear_model import LogisticRegression

logmodel = LogisticRegression()
logmodel.fit(X_train_1, Y_train_1)

In the above snippet, we’re splitting the primary data & create a set of test & train data. Once we have the collection, the application will put the logistic regression model. And, finally, we’ll fit the training data.

# Adding Predictions to it
predictions_1 = logmodel.predict(X_test_1)

from sklearn.metrics import classification_report

print('Classification Report:: ')
print(classification_report(Y_test_1, predictions_1))

The above lines, finally use the model & then we feed our test data.

Let’s see how it runs –

5.1.Run_Windows
5.2. Run_Windows

And, here is the log directory –

4. Logs

For better understanding, I’m just clubbing both the diagram at one place & the final outcome is showing as follows –

1. MergeReport

So, from the above picture, we can see that the maximum vulnerable patients are patients who are 80+. The next two categories that also suffered are 70+ & 60+.

Also, We’ve checked the Female Vs. Male in the following code –

sns.countplot(x='Survived', hue='Female', data=df_5, palette='RdBu_r')
plt.xlabel("Survived(0 - Died   Vs    1 - Alive)")
plt.ylabel("Female Vs Male (Including Other Genders)")

And, the analysis represents through this –

8. Female_Male

In this case, you have to consider that the Male part includes all the other genders apart from the actual Male. Hence, I believe death for females would be more compared to people who identified themselves as males.

So, finally, we’ve done it.

During this challenging time, I would request you to follow strict health guidelines & stay healthy.

N.B.: All the data that are used here can be found in the public domain. We use this solely for educational purposes. You can find the details here.

Predicting Flipkart business growth factor using Linear-Regression Machine Learning Model

Hi Guys,

Today, We’ll be exploring the potential business growth factor using the “Linear-Regression Machine Learning” model. We’ve prepared a set of dummy data & based on that, we’ll predict.

Let’s explore a few sample data –

1. Sample Data

So, based on these data, we would like to predict YearlyAmountSpent dependent on any one of the following features, i.e. [ Time On App / Time On Website / Flipkart Membership Duration (In Year) ].

You need to install the following packages –

pip install pandas

pip install matplotlib

pip install sklearn

We’ll be discussing only the main calling script & class script. However, we’ll be posting the parameters without discussing it. And, we won’t discuss clsL.py as we’ve already discussed that in our previous post.

1. clsConfig.py (This script contains all the parameter details.)

################################################
#### Written By: SATYAKI DE                 ####
#### Written On: 15-May-2020                ####
####                                        ####
#### Objective: This script is a config     ####
#### file, contains all the keys for        ####
#### Machine-Learning. Application will     ####
#### process these information & perform    ####
#### various analysis on Linear-Regression. ####
################################################

import os
import platform as pl

class clsConfig(object):
    Curr_Path = os.path.dirname(os.path.realpath(__file__))

    os_det = pl.system()
    if os_det == "Windows":
        sep = '\\'
    else:
        sep = '/'

    config = {
        'APP_ID': 1,
        'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
        'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
        'LOG_PATH': Curr_Path + sep + 'log' + sep,
        'REPORT_PATH': Curr_Path + sep + 'report',
        'FILE_NAME': Curr_Path + sep + 'Data' + sep + 'FlipkartCustomers.csv',
        'SRC_PATH': Curr_Path + sep + 'Data' + sep,
        'APP_DESC_1': 'IBM Watson Language Understand!',
        'DEBUG_IND': 'N',
        'INIT_PATH': Curr_Path
    }

2. clsLinearRegression.py (This is the main script, which will invoke the Machine-Learning API & return 0 if successful.)

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 15-May-2020              ####
#### Modified On 15-May-2020              ####
####                                      ####
#### Objective: Main scripts for Linear   ####
#### Regression.                          ####
##############################################

import pandas as p
import numpy as np
import regex as re

import matplotlib.pyplot as plt
from clsConfig import clsConfig as cf

# %matplotlib inline -- for Jupyter Notebook
class clsLinearRegression:
    def __init__(self):
        self.fileName =  cf.config['FILE_NAME']

    def predictResult(self):
        try:

            inputFileName = self.fileName

            # Reading from Input File
            df = p.read_csv(inputFileName)

            print()
            print('Projecting sample rows: ')
            print(df.head())

            print()
            x_row = df.shape[0]
            x_col = df.shape[1]

            print('Total Number of Rows: ', x_row)
            print('Total Number of columns: ', x_col)

            # Adding Features
            x = df[['TimeOnApp', 'TimeOnWebsite', 'FlipkartMembershipInYear']]

            # Target Variable - Trying to predict
            y = df['YearlyAmountSpent']

            # Now Train-Test Split of your source data
            from sklearn.model_selection import train_test_split

            # test_size => % of allocated data for your test cases
            # random_state => A specific set of random split on your data
            X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.4, random_state=101)

            # Importing Model
            from sklearn.linear_model import LinearRegression

            # Creating an Instance
            lm = LinearRegression()

            # Train or Fit my model on Training Data
            lm.fit(X_train, Y_train)

            # Creating a prediction value
            flipKartSalePrediction = lm.predict(X_test)

            # Creating a scatter plot based on Actual Value & Predicted Value
            plt.scatter(Y_test, flipKartSalePrediction)

            # Adding meaningful Label
            plt.xlabel('Actual Values')
            plt.ylabel('Predicted Values')

            # Checking Individual Metrics
            from sklearn import metrics

            print()
            mea_val = metrics.mean_absolute_error(Y_test, flipKartSalePrediction)
            print('Mean Absolute Error (MEA): ', mea_val)

            mse_val = metrics.mean_squared_error(Y_test, flipKartSalePrediction)
            print('Mean Square Error (MSE): ', mse_val)

            rmse_val = np.sqrt(metrics.mean_squared_error(Y_test, flipKartSalePrediction))
            print('Square root Mean Square Error (RMSE): ', rmse_val)

            print()

            # Check Variance Score - R^2 Value
            print('Variance Score:')
            var_score = str(round(metrics.explained_variance_score(Y_test, flipKartSalePrediction) * 100, 2)).strip()
            print('Our Model is', var_score, '% accurate. ')
            print()

            # Finding Coeficent on X_train.columns
            print()
            print('Finding Coeficent: ')

            cedf = p.DataFrame(lm.coef_, x.columns, columns=['Coefficient'])
            print('Printing the All the Factors: ')
            print(cedf)

            print()

            # Getting the Max Value from it
            cedf['MaxFactorForBusiness'] = cedf['Coefficient'].max()

            # Filtering the max Value to identify the biggest Business factor
            dfMax = cedf[(cedf['MaxFactorForBusiness'] == cedf['Coefficient'])]

            # Dropping the derived column
            dfMax.drop(columns=['MaxFactorForBusiness'], inplace=True)
            dfMax = dfMax.reset_index()

            print(dfMax)

            # Extracting Actual Business Factor from Pandas dataframe
            str_factor_temp = str(dfMax.iloc[0]['index'])
            str_factor = re.sub("([a-z])([A-Z])", "\g<1> \g<2>", str_factor_temp)
            str_value = str(round(float(dfMax.iloc[0]['Coefficient']),2))

            print()
            print('*' * 80)
            print('Major Busienss Activity - (', str_factor, ') - ', str_value, '%')
            print('*' * 80)
            print()

            # This is require when you are trying to print from conventional
            # front & not using Jupyter notebook.
            plt.show()

            return 0

        except Exception  as e:
            x = str(e)
            print('Error : ', x)

            return 1

Key lines from the above snippet –

# Adding Features
x = df[['TimeOnApp', 'TimeOnWebsite', 'FlipkartMembershipInYear']]

Our application creating a subset of the main datagram, which contains all the features.

# Target Variable - Trying to predict
y = df['YearlyAmountSpent']

Now, the application is setting the target variable into ‘Y.’

# Now Train-Test Split of your source data
from sklearn.model_selection import train_test_split

# test_size => % of allocated data for your test cases
# random_state => A specific set of random split on your data
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.4, random_state=101)

As per “Supervised Learning,” our application is splitting the dataset into two subsets. One is to train the model & another segment is to test your final model. However, you can divide the data into three sets that include the performance statistics for a large dataset. In our case, we don’t need that as this data is significantly less.

# Train or Fit my model on Training Data
lm.fit(X_train, Y_train)

Our application is now training/fit the data into the model.

# Creating a scatter plot based on Actual Value & Predicted Value
plt.scatter(Y_test, flipKartSalePrediction)

Our application projected the outcome based on the predicted data in a scatterplot graph.

Also, the following concepts captured by using our program. For more details, I’ve provided the external link for your reference –

  1. Mean Absolute Error (MEA)
  2. Mean Square Error (MSE)
  3. Square Root Mean Square Error (RMSE)

And, the implementation has shown as –

mea_val = metrics.mean_absolute_error(Y_test, flipKartSalePrediction)
print('Mean Absolute Error (MEA): ', mea_val)

mse_val = metrics.mean_squared_error(Y_test, flipKartSalePrediction)
print('Mean Square Error (MSE): ', mse_val)

rmse_val = np.sqrt(metrics.mean_squared_error(Y_test, flipKartSalePrediction))
print('Square Root Mean Square Error (RMSE): ', rmse_val)

At this moment, we would like to check the credibility of our model by using the variance score are as follows –

var_score = str(round(metrics.explained_variance_score(Y_test, flipKartSalePrediction) * 100, 2)).strip()
print('Our Model is', var_score, '% accurate. ')

Finally, extracting the coefficient to find out, which particular feature will lead Flikkart for better sale & growth by taking the maximum of coefficient value month the all features are as shown below –

cedf = p.DataFrame(lm.coef_, x.columns, columns=['Coefficient'])

# Getting the Max Value from it
cedf['MaxFactorForBusiness'] = cedf['Coefficient'].max()

# Filtering the max Value to identify the biggest Business factor
dfMax = cedf[(cedf['MaxFactorForBusiness'] == cedf['Coefficient'])]

# Dropping the derived column
dfMax.drop(columns=['MaxFactorForBusiness'], inplace=True)
dfMax = dfMax.reset_index()

Note that we’ve used a regular expression to split the camel-case column name from our feature & represent that with a much more meaningful name without changing the column name.

# Extracting Actual Business Factor from Pandas dataframe
str_factor_temp = str(dfMax.iloc[0]['index'])
str_factor = re.sub("([a-z])([A-Z])", "\g<1> \g<2>", str_factor_temp)
str_value = str(round(float(dfMax.iloc[0]['Coefficient']),2))

print('Major Busienss Activity - (', str_factor, ') - ', str_value, '%')

3. callLinear.py (This is the first calling script.)

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 15-May-2020              ####
#### Modified On 15-May-2020              ####
####                                      ####
#### Objective: Main calling scripts.     ####
##############################################

from clsConfig import clsConfig as cf
import clsL as cl
import logging
import datetime
import clsLinearRegression as cw

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

# Lookup functions from
# Azure cloud SQL DB

var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

def main():
    try:
        ret_1 = 0
        general_log_path = str(cf.config['LOG_PATH'])

        # Enabling Logging Info
        logging.basicConfig(filename=general_log_path + 'MachineLearning_LinearRegression.log', level=logging.INFO)

        # Initiating Log Class
        l = cl.clsL()

        # Moving previous day log files to archive directory
        log_dir = cf.config['LOG_PATH']
        curr_ver =datetime.datetime.now().strftime("%Y-%m-%d")

        tmpR0 = "*" * 157

        logging.info(tmpR0)
        tmpR9 = 'Start Time: ' + str(var)
        logging.info(tmpR9)
        logging.info(tmpR0)

        print("Log Directory::", log_dir)
        tmpR1 = 'Log Directory::' + log_dir
        logging.info(tmpR1)

        print('Machine Learning - Linear Regression Prediction : ')
        print('-' * 200)

        # Create the instance of the Linear-Regression Class
        x2 = cw.clsLinearRegression()

        ret = x2.predictResult()

        if ret == 0:
            print('Successful Linear-Regression Prediction Generated!')
        else:
            print('Failed to generate Linear-Regression Prediction!')

        print("-" * 200)
        print()

        print('Finding Analysis points..')
        print("*" * 200)
        logging.info('Finding Analysis points..')
        logging.info(tmpR0)


        tmpR10 = 'End Time: ' + str(var)
        logging.info(tmpR10)
        logging.info(tmpR0)

    except ValueError as e:
        print(str(e))
        logging.info(str(e))

    except Exception as e:
        print("Top level Error: args:{0}, message{1}".format(e.args, e.message))

if __name__ == "__main__":
    main()

Key snippet from the above script –

# Create the instance of the Linear-Regression
x2 = cw.clsLinearRegression()

ret = x2.predictResult()

In the above snippet, our application initially creating an instance of the main class & finally invokes the “predictResult” method.

Let’s run our application –

Step 1:

First, the application will fetch the following sample rows from our source file – if it is successful.

2. Run_1

Step 2:

Then, It will create the following scatterplot by executing the following snippet –

# Creating a scatter plot based on Actual Value & Predicted Value
plt.scatter(Y_test, flipKartSalePrediction)
3. Run_2

Note that our model is pretty accurate & it has a balanced success rate compared to our predicted numbers.

Step 3:

Finally, it is successfully able to project the critical feature are shown below –

4. Run_3

From the above picture, you can see that our model is pretty accurate (89% approx).

Also, highlighted red square identifying the key-features & their confidence score & finally, the projecting the winner feature marked in green.

So, as per that, we’ve come to one conclusion that Flipkart’s business growth depends on the tenure of their subscriber, i.e., old members are prone to buy more than newer members.

Let’s look into our directory structure –

5. Win_Dir

So, we’ve done it.

I’ll be posting another new post in the coming days. Till then, Happy Avenging! 😀

Note: All the data posted here are representational data & available over the internet & for educational purpose only.

Prepare analytics based on streaming data from Twitter using Python

Hi Guys!

Today, we will be projecting an analytics storyline based on streaming data from twitter’s developer account.

I want to make sure that this solely for educational purposes & no data analysis has provided to any agency or third-party apps. So, when you are planning to use this API, make sure that you strictly follow these rules.

In order to create a streaming channel from Twitter, you need to create one developer account.

As I’m a huge soccer fan, I would like to refer to one soccer place on Twitter for this. In this case, we’ll be checking BA Analytics for this.

6. Origin_Site

Please find the steps to create one developer account –

Step -1: 

You have to go to the following link. Over there you need to submit the request in order to create the account. You need to provide proper justification as to why you need that account. I’m not going into those forms. They are self-explanatory.

Once, your developer account activated, you need to click the following link as shown below –

1. TwitterSetup

Once you clicked that, the program will lead to you the following page –

2. TwitterSetup - Continue

If you don’t have any app, the first page will look something like the above page.

Step 2:

3. TwiterSetup - Continue

Now, you need to fill-up the following details. For security reasons, I’ll be hiding sensitive data here.

Step 3:

4. TwitterSetUp - Continue

After creating that, you need to go to the next tab i.e. key’s & tokens. The initial screen will only have Consumer API keys.

Step 4:

To generate the Access token, you need to click the create button from the above screenshot & then the new page will look like this –

5. TwitterSetUp - Continue

Our program will be using all these pieces of information.

So, now we’re ready for our Python program.

In order to access Twitter API through python, you need to install the following package –

pip install python-twitter

Let’s see the directory structure –

7. Directory

Let’s check only the relevant scripts here. We’re not going to discuss the clsL.py as we’ve already discussed. Please refer to the old post.

1. clsConfig.py (This script contains all the parameters of the server.)

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 12-Oct-2019              ####
####                                      ####
#### Objective: This script is a config   ####
#### file, contains all the keys for      ####
#### azure cosmos db. Application will    ####
#### process these information & perform  ####
#### various CRUD operation on Cosmos DB. ####
##############################################

import os
import platform as pl

class clsConfig(object):
    Curr_Path = os.path.dirname(os.path.realpath(__file__))

    os_det = pl.system()
    if os_det == "Windows":
        sep = '\\'
    else:
        sep = '/'

    config = {
        'APP_ID': 1,
        'EMAIL_SRC_JSON_FILE': Curr_Path + sep + 'src_file' + sep + 'srcEmail.json',
        'TWITTER_SRC_JSON_FILE': Curr_Path + sep + 'src_file' + sep + 'srcTwitter.json',
        'HR_SRC_JSON_FILE': Curr_Path + sep + 'src_file' + sep + 'srcHR.json',
        'ACCESS_TOKEN': '99999999-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX',
        'ACCESS_SECRET': 'YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY',
        'CONSUMER_KEY': "aaaaaaaaaaaaaaaaaaaaaaa",
        'CONSUMER_SECRET': 'HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH',
        'ARCH_DIR': Curr_Path + sep + 'arch' + sep,
        'PROFILE_PATH': Curr_Path + sep + 'profile' + sep,
        'LOG_PATH': Curr_Path + sep + 'log' + sep,
        'REPORT_PATH': Curr_Path + sep + 'report',
        'APP_DESC_1': 'Feedback Communication',
        'DEBUG_IND': 'N',
        'INIT_PATH': Curr_Path
    }

For security reasons, I’ve removed the original keys with dummy keys. You have to fill-up your own keys.

2. clsTwitter.py (This script will fetch data from Twitter API & process the same & send it to the calling method.)

###############################################
#### Written By: SATYAKI DE                ####
#### Written On: 12-Oct-2019               ####
#### Modified On 12-Oct-2019               ####
####                                       ####
#### Objective: Main class fetching sample ####
#### data from Twitter API.                ####
###############################################

import twitter
from clsConfig import clsConfig as cf
import json
import re
import string
import logging

class clsTwitter:
    def __init__(self):
        self.access_token = cf.config['ACCESS_TOKEN']
        self.access_secret = cf.config['ACCESS_SECRET']
        self.consumer_key = cf.config['CONSUMER_KEY']
        self.consumer_secret = cf.config['CONSUMER_SECRET']

    def find_element(self, srcJson, key):
        """Pull all values of specified key from nested JSON."""
        arr = []

        def fetch(srcJson, arr, key):
            """Recursively search for values of key in JSON tree."""
            if isinstance(srcJson, dict):
                for k, v in srcJson.items():
                    if isinstance(v, (dict, list)):
                        fetch(v, arr, key)
                    elif k == key:
                        arr.append(v)
            elif isinstance(srcJson, list):
                for item in srcJson:
                    fetch(item, arr, key)
            return arr

        finJson = fetch(srcJson, arr, key)
        return finJson

    def searchQry(self, rawQry):
        try:
            fin_dict = {}
            finJson = ''
            res = ''
            cnt = 0

            # Parameters to invoke Twitter API
            ACCESS_TOKEN = self.access_token
            ACCESS_SECRET = self.access_secret
            CONSUMER_KEY = self.consumer_key
            CONSUMER_SECRET = self.consumer_secret

            tmpR20 = 'Raw Query: ' + str(rawQry)
            logging.info(tmpR20)

            finJson = '['

            if rawQry == '':
                print('No data to proceed!')
                logging.info('No data to proceed!')
            else:
                t = twitter.Api(
                                  consumer_key = CONSUMER_KEY,
                                  consumer_secret = CONSUMER_SECRET,
                                  access_token_key = ACCESS_TOKEN,
                                  access_token_secret = ACCESS_SECRET
                               )

                response = t.GetSearch(raw_query=rawQry)
                print('Total Records fetched:', str(len(response)))

                for i in response:

                    # Converting them to json
                    data = str(i)
                    res_json = json.loads(data)

                    # Calling individual key
                    id = res_json['id']
                    tmpR19 = 'Id: ' + str(id)
                    logging.info(tmpR19)

                    try:
                        f_count = res_json['quoted_status']['user']['followers_count']
                    except:
                        f_count = 0
                    tmpR21 = 'Followers Count: ' + str(f_count)
                    logging.info(tmpR21)

                    try:
                        r_count = res_json['quoted_status']['retweet_count']
                    except:
                        r_count = 0
                    tmpR22 = 'Retweet Count: ' + str(r_count)
                    logging.info(tmpR22)

                    text = self.find_element(res_json, 'text')

                    for j in text:
                        strF = re.sub(f'[^{re.escape(string.printable)}]', '', str(j))
                        pat = re.compile(r'[\t\n]')
                        strG = pat.sub("", strF)
                        res = "".join(strG)

                    # Forming return dictionary
                    #fin_dict.update({id:'id', f_count: 'followerCount', r_count: 'reTweetCount', res: 'msgPost'})
                    if cnt == 0:
                        finJson = finJson + '{"id":' + str(id) + ',"followerCount":' + str(f_count) + ',"reTweetCount":' + str(r_count) + ', "msgPost":"' + str(res) + '"}'
                    else:
                        finJson = finJson + ', {"id":' + str(id) + ',"followerCount":' + str(f_count) + ',"reTweetCount":' + str(r_count) + ', "msgPost":"' + str(res) + '"}'

                    cnt += 1

            finJson = finJson + ']'

            jdata = json.dumps(finJson)
            ResJson = json.loads(jdata)

            return ResJson

        except Exception as e:
            ResJson = ''
            x = str(e)
            print(x)

            logging.info(x)
            ResJson = {'errorDetails' : x}

            return ResJson

The key lines from this snippet are as follows –

def find_element(self, srcJson, key):
    """Pull all values of specified key from nested JSON."""
    arr = []

    def fetch(srcJson, arr, key):
        """Recursively search for values of key in JSON tree."""
        if isinstance(srcJson, dict):
            for k, v in srcJson.items():
                if isinstance(v, (dict, list)):
                    fetch(v, arr, key)
                elif k == key:
                    arr.append(v)
        elif isinstance(srcJson, list):
            for item in srcJson:
                fetch(item, arr, key)
        return arr

    finJson = fetch(srcJson, arr, key)
    return finJson

This function will check against a specific key & based on that it will search from the supplied JSON & returns the value. This would be particularly very useful when you don’t have any fixed position of your elements.

t = twitter.Api(
                  consumer_key = CONSUMER_KEY,
                  consumer_secret = CONSUMER_SECRET,
                  access_token_key = ACCESS_TOKEN,
                  access_token_secret = ACCESS_SECRET
               )

response = t.GetSearch(raw_query=rawQry)

In this case, Python application will receive the JSON response using the new Twitter API.

id = res_json['id']
try:
    f_count = res_json['quoted_status']['user']['followers_count']
except:
    f_count = 0
try:
    r_count = res_json['quoted_status']['retweet_count']
except:
    r_count = 0

Fetching specific fixed position elements from the response API.

text = self.find_element(res_json, 'text')

Fetching the dynamic position based element using our customized function.

for j in text:
    strF = re.sub(f'[^{re.escape(string.printable)}]', '', str(j))
    pat = re.compile(r'[\t\n]')
    strG = pat.sub("", strF)
    res = "".join(strG)

Removing non-printable characters & white spaces from the extracted text field in order to get clean data.

if cnt == 0:
    finJson = finJson + '{"id":' + str(id) + ',"followerCount":' + str(f_count) + ',"reTweetCount":' + str(r_count) + ', "msgPost":"' + str(res) + '"}'
else:
    finJson = finJson + ', {"id":' + str(id) + ',"followerCount":' + str(f_count) + ',"reTweetCount":' + str(r_count) + ', "msgPost":"' + str(res) + '"}'

Finally, generating a JSON string dynamically.

jdata = json.dumps(finJson)
ResJson = json.loads(jdata)

And, returning the JSON to our calling program.

3. callTwitterAPI.py (This is the main script that will invoke the Twitter API & then project the analytic report based on the available Twitter data.)

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 12-Oct-2019              ####
#### Modified On 12-Oct-2019              ####
####                                      ####
#### Objective: Main calling scripts.     ####
##############################################

from clsConfig import clsConfig as cf
import pandas as p
import clsL as cl
import logging
import datetime
import json
import clsTwitter as ct

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

def getMaximumFollower(df):
    try:
        d1 = df['followerCount'].max()
        d1_max_str = int(d1)

        return d1_max_str
    except Exception as e:
        x = str(e)
        print(x)
        dt_part1 = 0

        return dt_part1

def getMaximumRetweet(df):
    try:
        d1 = df['reTweetCount'].max()
        d1_max_str = int(d1)

        return d1_max_str
    except Exception as e:
        x = str(e)
        print(x)
        dt_part1 = ''

        return dt_part1

# Lookup functions from
# Azure cloud SQL DB

var = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

def main():
    try:
        dfSrc = p.DataFrame()
        df_ret = p.DataFrame()
        ret_2 = ''
        debug_ind = 'Y'

        general_log_path = str(cf.config['LOG_PATH'])

        # Enabling Logging Info
        logging.basicConfig(filename=general_log_path + 'consolidatedTwitter.log', level=logging.INFO)

        # Initiating Log Class
        l = cl.clsL()

        # Moving previous day log files to archive directory
        arch_dir = cf.config['ARCH_DIR']
        log_dir = cf.config['LOG_PATH']

        tmpR0 = "*" * 157

        logging.info(tmpR0)
        tmpR9 = 'Start Time: ' + str(var)
        logging.info(tmpR9)
        logging.info(tmpR0)

        print("Archive Directory:: ", arch_dir)
        print("Log Directory::", log_dir)
        tmpR1 = 'Log Directory::' + log_dir
        logging.info(tmpR1)

        # Query using parameters
        rawQry = 'q=from%3ABlades_analytic&src=typd'

        x1 = ct.clsTwitter()
        ret_2 = x1.searchQry(rawQry)

        # Capturing the JSON Payload
        res = json.loads(ret_2)

        # Converting dictionary to Pandas Dataframe
        df_ret = p.read_json(ret_2, orient='records')

        # Resetting the column orders as per JSON
        df_ret = df_ret[list(res[0].keys())]

        l.logr('1.Twitter_' + var + '.csv', debug_ind, df_ret, 'log')

        print('Realtime Twitter Data:: ')
        logging.info('Realtime Twitter Data:: ')
        print(df_ret)
        print()

        # Checking execution status
        ret_val_2 = df_ret.shape[0]

        if ret_val_2 == 0:
            print("Twitter hasn't returned any rows. Please check your queries!")
            logging.info("Twitter hasn't returned any rows. Please check your queries!")
            print("*" * 157)
            logging.info(tmpR0)
        else:
            print("Successfuly row feteched!")
            logging.info("Successfuly row feteched!")
            print("*" * 157)
            logging.info(tmpR0)

        print('Finding Story points..')
        print("*" * 157)
        logging.info('Finding Story points..')
        logging.info(tmpR0)

        # Performing Basic Aggregate
        # 1. Find the user who has maximum Followers
        df_ret['MaxFollower'] = getMaximumFollower(df_ret)

        # 2. Find the user who has maximum Re-Tweets
        df_ret['MaxTweet'] = getMaximumRetweet(df_ret)

        # Getting Status
        df_MaxFollower = df_ret[(df_ret['followerCount'] == df_ret['MaxFollower'])]

        # Dropping Columns
        df_MaxFollower.drop(['reTweetCount'], axis=1, inplace=True)
        df_MaxFollower.drop(['MaxTweet'], axis=1, inplace=True)

        l.logr('2.Twitter_Maximum_Follower_' + var + '.csv', debug_ind, df_MaxFollower, 'log')

        print('Maximum Follower:: ')
        print(df_MaxFollower)
        print("*" * 157)
        logging.info(tmpR0)

        df_MaxTwitter = df_ret[(df_ret['reTweetCount'] == df_ret['MaxTweet'])]
        print()

        # Dropping Columns
        df_MaxTwitter.drop(['followerCount'], axis=1, inplace=True)
        df_MaxTwitter.drop(['MaxFollower'], axis=1, inplace=True)

        l.logr('3.Twitter_Maximum_Retweet_' + var + '.csv', debug_ind, df_MaxTwitter, 'log')

        print('Maximum Re-Twitt:: ')
        print(df_MaxTwitter)
        print("*" * 157)
        logging.info(tmpR0)

        tmpR10 = 'End Time: ' + str(var)
        logging.info(tmpR10)
        logging.info(tmpR0)

    except ValueError:
        print("No relevant data to proceed!")
        logging.info("No relevant data to proceed!")

    except Exception as e:
        print("Top level Error: args:{0}, message{1}".format(e.args, e.message))

if __name__ == "__main__":
    main()

And, here are the key lines –

x1 = ct.clsTwitter()
ret_2 = x1.searchQry(rawQry)

Our application is instantiating the newly developed class.

# Capturing the JSON Payload
res = json.loads(ret_2)

# Converting dictionary to Pandas Dataframe
df_ret = p.read_json(ret_2, orient='records')

# Resetting the column orders as per JSON
df_ret = df_ret[list(res[0].keys())]

Converting the JSON to pandas dataframe for our analytic data point.

def getMaximumFollower(df):
    try:
        d1 = df['followerCount'].max()
        d1_max_str = int(d1)

        return d1_max_str
    except Exception as e:
        x = str(e)
        print(x)
        dt_part1 = 0

        return dt_part1

def getMaximumRetweet(df):
    try:
        d1 = df['reTweetCount'].max()
        d1_max_str = int(d1)

        return d1_max_str
    except Exception as e:
        x = str(e)
        print(x)
        dt_part1 = ''

        return dt_part1

These two functions declared above in the calling script are generating the maximum data point from the Re-Tweet & Followers from our returned dataset.

# Getting Status
df_MaxFollower = df_ret[(df_ret['followerCount'] == df_ret['MaxFollower'])]

And, this is the way, our application will fetch the maximum twitter dataset –

df_MaxTwitter = df_ret[(df_ret['reTweetCount'] == df_ret['MaxTweet'])]

And, you can customize your output by dropping unwanted columns in the specific dataset.

And, here is the output on Windows, which looks like –

8. WindowsRun

And, here is the windows log directory –

WindowsRunLog

So, we’ve achieved our target data point.

So, we’ll come out with another exciting post in the coming days!

N.B.: This is demonstrated for RnD/study purposes. All the data posted here are representational data & available over the internet.

Explaining New Python Library – Regular Expression in JSON

Hi Guys!

As discussed, here is the continuation of the previous post. We’ll explain the regular expression from the library that I’ve created recently.

First, let me share the calling script for regular expression –

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 08-Sep-2019              ####
####                                      ####
#### Objective: Main calling scripts.     ####
##############################################

from dnpr.clsDnpr import clsDnpr
import datetime as dt
import json

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

# Lookup functions from
# Azure cloud SQL DB

def main():
    try:
        # Initializing the class
        t = clsDnpr()
        
        srcJson = [
                    {"FirstName": "Satyaki", "LastName": "De", "Sal": 1000},
                    {"FirstName": "Satyaki", "LastName": "De", "Sal": 1000},
                    {"FirstName": "Archi", "LastName": "Bose", "Sal": 500},
                    {"FirstName": "Archi", "LastName": "Bose", "Sal": 7000},
                    {"FirstName": "Deb", "LastName": "Sen", "Sal": 9500}
                  ]

        print("4. Checking regular expression functionality!")
        print()

        var13 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var13))

        print('::Function Regex_Like:: ')
        print()

        tarColumn = 'FirstName'
        print('Target Column for Rexex_Like: ', tarColumn)
        inpPattern = r"\bSa"
        print('Input Pattern: ', str(inpPattern))

        # Invoking the distinct function
        tarJson = t.regex_like(srcJson, tarColumn, inpPattern)

        print('End of Function Regex_Like!')
        print()

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat = json.dumps(tarJson, indent=1)
        print(str(tarJsonFormat))
        print("*" * 157)

        if not tarJson:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var14 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var14))

        var15 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var15))

        print('::Function Regex_Replace:: ')
        print()

        tarColumn = 'FirstName'
        print('Target Column for Rexex_Replace: ', tarColumn)
        inpPattern = r"\bSa"
        print('Input Pattern: ', str(inpPattern))
        replaceString = 'Ka'
        print('Replacing Character: ', replaceString)

        # Invoking the distinct function
        tarJson = t.regex_replace(srcJson, tarColumn, inpPattern, replaceString)

        print('End of Function Rexex_Replace!')
        print()

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat = json.dumps(tarJson, indent=1)
        print(str(tarJsonFormat))
        print("*" * 157)

        if not tarJson:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var16 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var16))

        var17 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var17))

        print('::Function Regex_Substr:: ')
        print()

        tarColumn = 'FirstName'
        print('Target Column for Regex_Substr: ', tarColumn)
        inpPattern = r"\bSa"
        print('Input Pattern: ', str(inpPattern))

        # Invoking the distinct function
        tarJson = t.regex_substr(srcJson, tarColumn, inpPattern)

        print('End of Function Regex_Substr!')
        print()

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat = json.dumps(tarJson, indent=1)
        print(str(tarJsonFormat))
        print("*" * 157)

        if not tarJson:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var18 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var18))

        print("=" * 157)
        print("End of regular expression function!")
        print("=" * 157)



    except ValueError:
        print("No relevant data to proceed!")

    except Exception as e:
        print("Top level Error: args:{0}, message{1}".format(e.args, e.message))

if __name__ == "__main__":
    main()

As per the library, we’ll discuss the following functionalities –

  1. regex_like
  2. regex_replace
  3. regex_substr

Now, let us check how to call these functions.

1. regex_like:

Following is the base skeleton in order to invoke this function –

regex_like(Input Json, Target Column, Pattern To Match) return Output Json

Here are the key lines in the script –

srcJson = [
            {"FirstName": "Satyaki", "LastName": "De", "Sal": 1000},
            {"FirstName": "Satyaki", "LastName": "De", "Sal": 1000},
            {"FirstName": "Archi", "LastName": "Bose", "Sal": 500},
            {"FirstName": "Archi", "LastName": "Bose", "Sal": 7000},
            {"FirstName": "Deb", "LastName": "Sen", "Sal": 9500}
          ]

# Invoking the distinct function
tarJson = t.regex_like(srcJson, tarColumn, inpPattern)

2. regex_replace:

Following is the base skeleton in order to invoke this function –

regex_replace(Input Json, Target Column, Pattern to Replace) return Output Json

Here are the key lines in the script –

tarColumn = 'FirstName'
print('Target Column for Rexex_Replace: ', tarColumn)
inpPattern = r"\bSa"
print('Input Pattern: ', str(inpPattern))
replaceString = 'Ka'
print('Replacing Character: ', replaceString)

# Invoking the distinct function
tarJson = t.regex_replace(srcJson, tarColumn, inpPattern, replaceString)

As you can see, here ‘Sa’ with ‘Ka’ provided it matches the specific pattern in the JSON.

3. regex_replace:

Following is the base skeleton in order to invoke this function –

regex_substr(Input Json, Target Column, Pattern to substring) return Output Json

Here are the key lines –

tarColumn = 'FirstName'
print('Target Column for Regex_Substr: ', tarColumn)
inpPattern = r"\bSa"
print('Input Pattern: ', str(inpPattern))

# Invoking the distinct function
tarJson = t.regex_substr(srcJson, tarColumn, inpPattern)

In this case, we’ve subtracted a part of the JSON string & return the final result as JSON.

Let us first see the sample input JSON –

SourceJSON_Regex

Let us check how it looks when we run the calling script –

  • regex_like:
Regex_Like

This function will retrieve the elements, which will start with ‘Sa‘. As a result, we’ll see the following two elements in the Payload.

  • regex_replace:
Regex_Replace

In this case, we’re replacing any string which starts with ‘Sa‘ & replaced with the ‘Ka‘.

  • regex_substr:
Regex_Substr

As you can see that the first element FirstName changed the name from “Satyaki” to “tyaki“.

So, finally, we’ve achieved our target.

I’ll post the next exciting concept very soon.

Till then! Happy Avenging! 😀

N.B.: This is demonstrated for RnD/study purposes. All the data posted here are representational data & available over the internet.

Explaining new Python library

Hi Guys!

Here is the post as to how to call this Dnpr library & what are the current limitations of this library.

Before we start let’s post the calling script & then explain how we can use them –

##############################################
#### Written By: SATYAKI DE               ####
#### Written On: 08-Sep-2019              ####
####                                      ####
#### Objective: Main calling scripts.     ####
##############################################

from dnpr.clsDnpr import clsDnpr
import datetime as dt
import json

# Disbling Warning
def warn(*args, **kwargs):
    pass

import warnings
warnings.warn = warn

# Lookup functions from


def main():
    try:
        srcJson = [
                    {"FirstName": "Satyaki", "LastName": "De", "Sal": 1000},
                    {"FirstName": "Satyaki", "LastName": "De", "Sal": 1000},
                    {"FirstName": "Archi", "LastName": "Bose", "Sal": 500},
                    {"FirstName": "Archi", "LastName": "Bose", "Sal": 7000},
                    {"FirstName": "Deb", "LastName": "Sen", "Sal": 9500}
                  ]

        print("=" * 157)
        print("Checking distinct function!")
        print("=" * 157)
        print()

        print("*" * 157)
        print("Input Data: ")
        srcJsonFormat = json.dumps(srcJson, indent=1)
        print(str(srcJsonFormat))
        print("*" * 157)

        # Initializing the class
        t = clsDnpr()

        print("1. Checking distinct functionality!")

        var1 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var1))

        # Invoking the distinct function
        tarJson = t.distinct(srcJson)

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat = json.dumps(tarJson, indent=1)
        print(str(tarJsonFormat))
        print("*" * 157)

        if not tarJson:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var2 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var2))

        print("=" * 157)
        print("End of distinct function!")
        print("=" * 157)

        print("2. Checking nvl functionality!")

        srcJson_1 = [
            {"FirstName": "Satyaki", "LastName": "", "Sal": 1000},
            {"FirstName": "Archi", "LastName": "Bose", "Sal": 500},
            {"FirstName": "Deb", "LastName": "", "Sal": 9500}
        ]

        var3 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var3))

        strDef = 'FNU'
        print("Default Value: ", strDef)
        srcColName = 'LastName'
        print('Candidate Column for NVL: ', srcColName)

        # Invoking the nvl function
        tarJson_1 = t.nvl(srcJson_1, srcColName, strDef)

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat_1 = json.dumps(tarJson_1, indent=1)
        print(str(tarJsonFormat_1))
        print("*" * 157)

        if not tarJson_1:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var4 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var4))

        print("=" * 157)
        print("End of nvl function!")
        print("=" * 157)

        print("3. Checking partition-by functionality!")

        srcJson_2 = [
            {"FirstName": "Satyaki", "LastName": "", "Sal": 1000},
            {"FirstName": "Satyaki", "LastName": "", "Sal": 700},
            {"FirstName": "Archi", "LastName": "Bose", "Sal": 500},
            {"FirstName": "Deb", "LastName": "", "Sal": 9500},
            {"FirstName": "Archi", "LastName": "Bose", "Sal": 4500},
        ]

        var5 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var5))

        GrList = ['FirstName', 'LastName']
        print("Partition By Columns::: ", str(GrList))
        grOperation = 'Max'
        print('Operation toe be performed: ', grOperation)
        strCandidateColumnName = 'Sal'
        print('Column Name on which the aggregate function will take place: ', strCandidateColumnName)

        # Invoking the partition by function - MAX
        tarJson_1 = t.partitionBy(srcJson_2, GrList, grOperation, strCandidateColumnName)

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat_1 = json.dumps(tarJson_1, indent=1)
        print(str(tarJsonFormat_1))
        print("*" * 157)

        if not tarJson_1:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var6 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var6))

        var7 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var7))

        grOperation_1 = 'Min'
        print('Operation toe be performed: ', grOperation_1)

        # Invoking the Partition By function - MIN
        tarJson_2 = t.partitionBy(srcJson_2, GrList, grOperation_1, strCandidateColumnName)

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat_2 = json.dumps(tarJson_2, indent=1)
        print(str(tarJsonFormat_2))
        print("*" * 157)

        if not tarJson_2:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var8 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var8))

        var9 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var9))

        grOperation_2 = 'Avg'
        print('Operation toe be performed: ', grOperation_2)

        # Invoking the Partition By function - Avg
        tarJson_3 = t.partitionBy(srcJson_2, GrList, grOperation_2, strCandidateColumnName)

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat_3 = json.dumps(tarJson_3, indent=1)
        print(str(tarJsonFormat_3))
        print("*" * 157)

        if not tarJson_3:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var10 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var10))

        var11 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var11))

        grOperation_3 = 'Sum'
        print('Operation toe be performed: ', grOperation_3)

        # Invoking the Partition By function - Sum
        tarJson_4 = t.partitionBy(srcJson_2, GrList, grOperation_3, strCandidateColumnName)

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat_4 = json.dumps(tarJson_4, indent=1)
        print(str(tarJsonFormat_4))
        print("*" * 157)

        if not tarJson_4:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var12 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var12))

        print("=" * 157)
        print("End of partition function!")
        print("=" * 157)

        print("4. Checking regular expression functionality!")
        print()

        var13 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var13))

        print('::Function Regex_Like:: ')
        print()

        tarColumn = 'FirstName'
        print('Target Column for Rexex_Like: ', tarColumn)
        inpPattern = r"\bSa"
        print('Input Pattern: ', str(inpPattern))

        # Invoking the regex_like function
        tarJson = t.regex_like(srcJson, tarColumn, inpPattern)

        print('End of Function Regex_Like!')
        print()

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat = json.dumps(tarJson, indent=1)
        print(str(tarJsonFormat))
        print("*" * 157)

        if not tarJson:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var14 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var14))

        var15 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var15))

        print('::Function Regex_Replace:: ')
        print()

        tarColumn = 'FirstName'
        print('Target Column for Rexex_Replace: ', tarColumn)
        inpPattern = r"\bSa"
        print('Input Pattern: ', str(inpPattern))
        replaceString = 'Ka'
        print('Replacing Character: ', replaceString)

        # Invoking the regex_replace function
        tarJson = t.regex_replace(srcJson, tarColumn, inpPattern, replaceString)

        print('End of Function Rexex_Replace!')
        print()

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat = json.dumps(tarJson, indent=1)
        print(str(tarJsonFormat))
        print("*" * 157)

        if not tarJson:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var16 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var16)) 

        var17 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("Start Time: ", str(var17))

        print('::Function Regex_Substr:: ')
        print()

        tarColumn = 'FirstName'
        print('Target Column for Regex_Substr: ', tarColumn)
        inpPattern = r"\bSa"
        print('Input Pattern: ', str(inpPattern))

        # Invoking the regex_substr function
        tarJson = t.regex_substr(srcJson, tarColumn, inpPattern)

        print('End of Function Regex_Substr!')
        print()

        print("*" * 157)
        print("Output Data: ")
        tarJsonFormat = json.dumps(tarJson, indent=1)
        print(str(tarJsonFormat))
        print("*" * 157)

        if not tarJson:
            print()
            print("No relevant output data!")
            print("*" * 157)
        else:
            print()
            print("Relevant output data comes!")
            print("*" * 157)

        var18 = dt.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
        print("End Time: ", str(var18))

        print("=" * 157)
        print("End of regular expression function!")
        print("=" * 157)


    except ValueError:
        print("No relevant data to proceed!")

    except Exception as e:
        print("Top level Error: args:{0}, message{1}".format(e.args, e.message))

if __name__ == "__main__":
    main()

Let’s explain the key lines –

As of now, the source payload that it will support is mostly simple JSON.

As you can see, we’ve relatively started with the simple JSON containing an array of elements.

# Initializing the class
t = clsDnpr()

In this line, you can initiate the main library.

Let’s explore the different functions, which you can use on JSON.

1. Distinct:

Let’s discuss the distinct function on  JSON. This function can be extremely useful if you use NoSQL, which doesn’t offer any distinct features. Or, if you are dealing with or expecting your source with duplicate JSON inputs.

Let’s check our sample payload for distinct –

Sample_Payload_Distinct

Here is the basic syntax & argument that it is expecting –

distinct(Input Json) returnOutput Json

So, all you have to ensure that you are passing a JSON input string.

As per our example –

# Invoking the distinct function
tarJson = t.distinct(srcJson)

And, here is the output –

Distinct_Output

If you compare the source JSON. You would have noticed that there are two identical entries with the name “Satyaki” is now replaced by one unique entries.

Limitation: Currently, this will support only basic JSON. However, I’m working on it to support that much more complex hierarchical JSON in coming days.

2. NVL:

NVL is another feature that I guess platform like JSON should have. So, I built this library specially handles the NULL data scenario, where the developer may want to pass a default value in place of NULL.

Hence, the implementation of this function.

Here is the sample payload for this –

Sample_Payload_NVL

In this case, if there is some business logic that requires null values replaced with some default value for LastName say e.g. FNU. This function will help you to implement that logic.

Here is the basic syntax & argument that it is expecting –

nvl(
     Input Json, 
     Prospective Null Column Name, 
     Dafult Value in case of Null
   ) return Output Json

And, here is the code implementation –

strDef = ‘FNU’
print(“Default Value: “, strDef)
srcColName = ‘LastName’
print(‘Candidate Column for NVL: ‘, srcColName)

# Invoking the nvl function
tarJson_1 = t.nvl(srcJson_1, srcColName, strDef)

So, in the above lines, this code will replace the Null value with the “FNU” for the column LastName.

And, Here is the output –

NVL_Output

3. Partition_By:

I personally like this function as this gives more power to manipulate any data in JSON levels such as Avg, Min, Max or Sum. This might be very useful in order to implement some basic aggregation on the fly.

Here is the basic syntax & argument that it is expecting –

partition_by(
              Input Json, 
              Group By Column List, 
              Group By Operation, 
              Candidate Column Name, 
              Output Column Name
            ) return Output Json

Now, we would explore the sample payload for all these functions to test –

Sample_payload_PartitionBy

Case 1:

In this case, we’ll calculate the maximum salary against FirstName & LastName. However, I want to print the Location in my final JSON output.

So, if you see the sample data & let’s make it tabular for better understanding –

PartitionMAX_SourceTab

So, as per our business logic, our MAX aggregate would operate only on FirstName & LastName. Hence, the calculation will process accordingly.

In that case, the output will look something like –

PartitionMAX_FinTab

As you can see, from the above picture two things happen. It will remove any duplicate entries. In this case, Satyaki has exactly two identical rows. So, it removes one. However, as part of partition by clause, it keeps two entries of Archi as the location is different. Deb will be appearing once as expected.

Let’s run our application & find out the output –

MAX_PartitionBy

So, we meet our expectation.

Case 2:

Same, logic will be applicable for Min as well.

Hence, as per the table, we should expect the output as –

PartitionMIN_FinTab

And, the output of our application run is –

MIN_PartitionBy

So, this also come as expected.

Case 3:

Let’s check for average –

PartitionAVG_FinTab

The only thing I wanted to point out, as we’ve two separate entries for Satyaki. So, the average will contain the salary from both the value as rightfully so. Hence, the average of (1000+700)/2 = 850.

Let’s run our application –

AVG_PartitionBy

So, we’ve achieved our target.

Case 4:

Let’s check for Sum.

PartitionSUM_FinTab

Now, let’s run our application –

SUM_PartitionBy

In the next installment, we’ll be discussing the last function from this package i.e. Regular Expression in JSON.

I hope, you’ll like this presentation.

Let me know – if you find any special bug. I’ll look into that.

Till then – Happy Avenging!

Note: All the data posted here are representational data & available over the internet & for educational purpose only.

Publishing new Python Library for JSON & NoSQL

Hi Guys!

As discussed,

Please find the link of the PyPI package of new enhanced JSON library on Python. This is particularly very useful as I’ve accommodated the following features into it.

  1. distinct
  2. nvl
  3. partition_by
  4. regex_like
  5. regex_replace
  6. regex_substr

All these functions can be used over JSON payload through python. I’ll discuss this in details in my next blog post.

However, I would like to suggest this library that will be handy for NoSQL databases like Cosmos DB. Now, you can quickly implement many of these features such as distinct, partitioning & regular expressions with less effort.

Please find the library URL.

Let me know your feedback on the same.

N.B.: I’ve tested this library both on Windows 10 & Ubuntu 18. And, the python version that I’ve used are Python3.6 & Python3.7.

Till then!

Happy Avenging!

Python Verse – Universe of Avengers in Computer Language World!

The last couple of years, I’ve been working on various technologies. And, one of the interesting languages that I came across is Python. It is extremely flexible for developers to learn & rapidly develop with very few lines of code compared to the other languages. There are major versions of python that I worked with. Among them, python 2.7 & current python 3.7.1 are very popular to developers & my personal favorite.

There are many useful packages that are available to reduce the burden of the developers. Among them, packages like “pandas”, “numpy”, “json”, “AES”, “threading” etc. are extremely useful & one can do lot’s of work with it.

I personally prefer Ubuntu or Mac version of python. However, I’ve worked on Windows version as well or developed python based framework & application, which works in all the major operating systems. If you take care few things from the beginning, then you don’t have to make much more changes of your python application in order to work in all the major operating systems. 🙂

To me, Python Universe is nothing shorter than Marvel’s Universe of Avengers. In order to beat Supreme Villain Thanos (That Challenging & Complex Product with extremely tight timeline), you got to have 6 infinity stones to defeat him.

  1. Space Stone ( Pandas & Numpy )
  2. Reality Stone ( Json, SSL & Encryption/Decryption )
  3. Power Stone ( Multi-Threading/Multi-Processing )
  4. Mind Stone ( OS, Database, Directories & Files )
  5. Soul Stone ( Logging & Exception )
  6. Time Stone ( Cloud Interaction & Framework )

I’ll release a series of python based post in coming days, which might be useful for many peers or information seeker. Hopefully, this installment is a beginning & please follow my post. I hope, very soon you will get many such useful posts.

You get the latest version of Python from the official site given below –

Python Link (3.7.1)

Make sure you must install pip package along with python. I’m not going in details of how one should install python in either of Windows/Mac or Linux.

Just showing you how to install individual python packages.

Windows:

pip install pandas

Linux/Mac:

sudo python3.7 -m pip install pandas

From the second example, you can see that you can install packages to specific python version in case if you have multiple versions of python.

Note that: There might be slight variation based on different versions of Linux. Make sure you are using the correct syntax as per your flavor.

You can get plenty of good sites, where the detailed step-by-step process shared for each operating system.

Till then – Happy Avenging!

Password Validation Using Regular Expression In Teradata 14 & 15

Today, we’ll be checking one new area where we can implement regular expression to achieve the password validation without involving any kind of Macro, Stored-Proc.

 

Let’s consider the following conditions to be implemented –

 

1. Password should contain characters between 6 & 10.

2. One character should be digit.

3. One character should be in upper case letter.

4. There should be at least one special character.

 

Let’s check the Query & Output –

 

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10
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select seq_no,
       passwd,
       regexp_similar(passwd,'^(?=^([[:graph:]]{6,10})$)(?=.*([[:upper:]]{1,}))(?=.*([[:digit:]]{1,})).*$') as reg_test
from scott.login_det
order by 1;


SEQ_NO	PASSWD	 REG_TEST
-----   -------  --------------
1	hoti         0
2	hotimla	     0
3	hotImla	     0
4	hot@imla     0
5	hoT@imla     0
6	hoT@iml9a    1
7	hoT@iml9a66  0

 

Similarly, you can add condition of lower case character if you want to make it more complex.

 

Hope, this will give you another way – to implement the same logic. 🙂