Day 17 : Visualization of data using matplotlib (corona virus analysis)

Hey Guys,
previously we learned about data manipulation and cleaning of data

Today we will learn about visualization of data using matplotlib

What is matplotlib?
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy.


Install matplotlib

pip install matplotlib


Let's have Fun with matplotlib


For dataset : https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset

Code:

 #import libraries
%matplotlib inline
import matplotlib.pyplot as plt   

import pandas as pd

#Read csv file which is get from kaggle dataset
df=pd.read_csv("corona/corona_data.csv")

print(df.head())

#This is used to filter data by country 
new_df=df.loc[df['Country/Region'] == "India"]
print(new_df)

#Here we get information about dates and confirmed cases
dates=new_df["ObservationDate"]

confirmed=new_df["Confirmed"]

#Basically as name says plot data 
#dates on x-axis
#confirmed on y-axis
#color your choice deafult is black
plt.plot(dates,confirmed,color="red")

#scale on x-axis  
#1st parameter is list of tick
#2nd is steps 
#3rd is rotate label
plt.xticks(range(0,new_df.shape[0],5),dates.loc[::5],rotation=90)

#x-axis lable

plt.xlabel('Date',fontsize=18)

#if you see all the data, you will find data is inconsistent 
somewhere it is female or Female  and other gender is 4000 
And we are dealing with computer so female and Female are different
We have to make that data consistant.
so replace female with Female
df=df.replace("female", "Female")

#here we get unique values for each gender or sex
m=df["sex"].value_counts()
print(m)

Male      707
Female    556
4000        1
Name: sex, dtype: int64

There is 4000 gender we have to remove this

df=df[df.sex!= "4000"]

#here we get unique values for each gender or sex
data=df["sex"].value_counts()
print(data)

Male      707
Female    556

Convert data into list for y-axis
count=list(data)

Gender for x-axis
gender=["Male","Female"]

plt.subplot(131)

#Plot bar chart
x-axis : gender
y-axis : gender count
plt.bar(gender, count)

Simple is that
You can have more fun...
Try for other countries and daily increase.

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