Pandas - GroupBy and Plot

Note

• This tutorial is also available on nbviewer, offering an alternative platform for your learning convenience.
• 🔥 Free Pandas Course: https://hedaro.gumroad.com/l/tqqfq

Some nifty ninjastics you can do with Group By and MatPlotLib.

import pandas as pd
from matplotlib.pylab import plt
import sys

---

Here is the csv data if you want to follow along:

Date,Symbol,Volume
1/1/2013,A,0
1/2/2013,A,200
1/3/2013,A,1200
1/4/2013,A,1001
1/5/2013,A,1300
1/6/2013,A,1350
3/8/2013,B,500
3/9/2013,B,1150
3/10/2013,B,1180
3/11/2013,B,2000
1/5/2013,C,56600
1/6/2013,C,45000
1/7/2013,C,200
5/20/2013,E,1300
5/21/2013,E,1700
5/22/2013,E,900
5/23/2013,E,2100
5/24/2013,E,8000
5/25/2013,E,12000
5/26/2013,E,1900
5/27/2013,E,1000
5/28/2013,E,1900

print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)

Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]
Pandas version 2.2.1

# let's see what kind of data we are working with
raw = pd.read_csv('Test_9_17_Python.csv')
raw.head()

	Date	Symbol	Volume
0	1/1/2013	A	0
1	1/2/2013	A	200
2	1/3/2013	A	1200
3	1/4/2013	A	1001
4	1/5/2013	A	1300

df2 = raw.copy()

You are going to have to change the data type of the Date column

df2.dtypes

Date      object
Symbol    object
Volume     int64
dtype: object

df2['Date'] = pd.to_datetime(df2['Date'])

df2.dtypes

Date      datetime64[ns]
Symbol            object
Volume             int64
dtype: object

# generate some fake data
pool = ['boy','girl']
pool = pool*(int(len(df2)/2))
df2['Gender'] = pool
df = df2.copy()
df.head()

	Date	Symbol	Volume	Gender
0	2013-01-01	A	0	boy
1	2013-01-02	A	200	girl
2	2013-01-03	A	1200	boy
3	2013-01-04	A	1001	girl
4	2013-01-05	A	1300	boy

Group one column and plot §

group = df.groupby('Symbol')

for x in group:
    print(type(x))
    print('//////')

<class 'tuple'>
//////
<class 'tuple'>
//////
<class 'tuple'>
//////
<class 'tuple'>
//////

fig, axes = plt.subplots(2,1, figsize=(15,5))
plt.subplots_adjust(hspace=0.5)
 
group.get_group('A').plot(ax=axes[0])
group.get_group('B').plot(ax=axes[1])
 
axes[0].set_title('title')
axes[0].set_xlabel('sdf')
 
axes[1].set_title('title bottom');

def plot(group):
    mask = group['Volume'].apply(lambda x: x>1000)
    mask2 = group['Symbol'] == 'A'
    mask3 = group['Symbol'] == 'B'
    
    return group[mask & (mask2 | mask3)]['Volume'].sum()
 
a = group[['Symbol','Volume']].apply(plot)
a = pd.DataFrame(a)
a = a.rename(columns={0:'Volume'})
a.plot();

for i, g in group:
    g.plot(title=i)

Group two columns and plot §

group = df.groupby(['Symbol', 'Gender'])

for i, g in group:
    print(i)

('A', 'boy')
('A', 'girl')
('B', 'boy')
('B', 'girl')
('C', 'boy')
('C', 'girl')
('E', 'boy')
('E', 'girl')

group.get_group(('A', 'boy')).plot()
group.get_group(('A', 'boy')).plot();

fig, axes = plt.subplots(len(group.groups),1, figsize=(5,20))
fig.subplots_adjust(hspace=1.0) ## Create space between plots
ix = 0
 
for i, g in group:
    p = g.plot(ax=axes[ix], title=str(i))
    if ix < len(axes)-1:
        ix = ix + 1
    else: 
        ix = 0