CODEWITHSUNDEEP
pythonpandasgroup-by
user3930865
user3930865

Reputation: 33

Plot bar graph using multiple groupby count in panda

I am trying to plot bar graph using pandas. DataTime is index column which I get from timestamp. Here is table structure:

table structure

So far i have written this:

import sqlite3
from pylab import *
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime as dt

conn = sqlite3.connect('DEMO2.sqlite')
df = pd.read_sql("SELECT * FROM Data", conn)
df['DateTime'] = df['DATE'].apply(lambda x: dt.date.fromtimestamp(x))

df1 = df.set_index('DateTime', drop=False)     

grouped= df1['DateTime'].groupby(lambda x: x.month)    
#df1.groupby([df1.index.month, 'DateTime']).count()    
grouped.count()

I want output like this:

date vs. X and Y

June has total 4 entry and one entry starts with u. so X has 4 y has 1. Same for July.

Also i want to plot bar graph (X and Y entries) using output. I want MONTH vs Values bar graph

Upvotes: 1

Views: 1526

Answers (1)

Andy Hayden
Andy Hayden

Reputation: 375475

I would created the DataFrame with a dict:

result = pd.DataFrame({'X': g.count(),
                       'Y': g.apply(lambda x: x.str.startswith('u').sum())})

Now you can use the plot method to plot months vs values.

result.plot()

Note: you can create grouped more efficiently:

grouped = df1['DateTime'].groupby(df1['DateTime'].dt.to_period('M'))

grouped = df1['DateTime'].groupby(df1['DateTime'].dt.month)  # if you want Jan-2015 == Jan-2014

Upvotes: 1

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