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in Technique[技术] by (71.8m points)

python - Bar graph from dataframe groupby

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv("arrests.csv")
df = df.replace(np.nan,0)
df = df.groupby(['home_team'])['arrests'].mean()

I'm trying to create a bar graph for dataframe. Under home_team are a bunch of team names. Under arrests are a number of arrests at each date. I've basically grouped the data by teams with the average arrests for that team. I'm trying to create a bar graph for this but am not sure how to proceed since one column doesn't have a header.

Data

home_team,arrests
Arizona,5.0
Arizona,6.0
Arizona,9.0
Arizona,6.0
Arizona,3.0
Arizona,4.0
Arizona,1.0
Arizona,4.0
Arizona,0.0
Arizona,12.0
Arizona,4.0
Arizona,1.0
Arizona,3.0
Arizona,7.0
Arizona,3.0
Arizona,7.0
Arizona,7.0
Arizona,3.0
Arizona,7.0
Arizona,2.0
Arizona,3.0
Arizona,2.0
Arizona,4.0
Arizona,7.0
Arizona,4.0
Arizona,6.0
Arizona,4.0
Arizona,2.0
Arizona,1.0
Arizona,6.0
Arizona,2.0
Arizona,4.0
Arizona,3.0
Arizona,10.0
Arizona,3.0
Arizona,2.0
Arizona,2.0
Arizona,0.0
Arizona,5.0
Arizona,2.0
Baltimore,1.0
Baltimore,0.0
Baltimore,0.0
Baltimore,0.0
Baltimore,2.0
Baltimore,0.0
Baltimore,0.0
Baltimore,0.0
Baltimore,3.0
Baltimore,1.0
Baltimore,0.0
Baltimore,3.0
Baltimore,5.0
Baltimore,0.0
Baltimore,8.0
Baltimore,0.0
Baltimore,4.0
Baltimore,5.0
Baltimore,0.0
Baltimore,0.0
Baltimore,0.0
Baltimore,1.0
Baltimore,0.0
Baltimore,0.0
Baltimore,3.0
Baltimore,0.0
Baltimore,6.0
Baltimore,0.0
Baltimore,0.0
Baltimore,0.0
Baltimore,4.0
Carolina,0.0
Carolina,0.0
Carolina,0.0
Carolina,0.0
Carolina,0.0
Carolina,0.0
Carolina,2.0
Carolina,0.0
Carolina,1.0
Carolina,1.0
Carolina,1.0
Carolina,4.0
Carolina,1.0
Carolina,0.0
Carolina,0.0
Carolina,1.0
Carolina,1.0
Carolina,5.0
Carolina,1.0
Carolina,3.0
Carolina,3.0
Carolina,0.0
Carolina,2.0
Carolina,1.0
Carolina,1.0
Carolina,5.0
Carolina,1.0
Carolina,2.0
Carolina,1.0
Carolina,0.0
Carolina,0.0
Carolina,0.0
Carolina,0.0
Carolina,0.0
Carolina,4.0
Carolina,6.0
Carolina,2.0
Carolina,3.0
Carolina,0.0
Carolina,3.0
Chicago,1.0
Chicago,0.0
Chicago,1.0
Chicago,0.0
Chicago,0.0
Chicago,0.0
Chicago,0.0
Chicago,1.0
Chicago,0.0
Chicago,1.0
Chicago,1.0
Chicago,0.0
Chicago,3.0
Chicago,1.0
Chicago,0.0
Chicago,2.0
Chicago,0.0
Chicago,0.0
Chicago,0.0
Chicago,2.0
Chicago,0.0
Chicago,2.0
Chicago,1.0
Chicago,2.0
Chicago,1.0
Chicago,1.0
Chicago,0.0
Chicago,2.0
Chicago,1.0
Chicago,0.0
Chicago,2.0
Chicago,1.0
Cincinnati,3.0
Cincinnati,0.0
Cincinnati,1.0
Cincinnati,2.0
Cincinnati,3.0
Cincinnati,0.0
Cincinnati,0.0
Cincinnati,0.0
Cincinnati,3.0
Cincinnati,0.0
Cincinnati,0.0
Cincinnati,2.0
Cincinnati,4.0
Cincinnati,3.0
Cincinnati,3.0
Cincinnati,1.0
Cincinnati,3.0
Cincinnati,1.0
Cincinnati,0.0
Cincinnati,1.0
Cincinnati,0.0
Cincinnati,1.0
Cincinnati,0.0
Cincinnati,0.0
Cincinnati,4.0
Cincinnati,0.0
Cincinnati,1.0
Cincinnati,1.0
Cincinnati,1.0
Cincinnati,10.0
Cincinnati,6.0
Cincinnati,0.0
Cincinnati,1.0
Cincinnati,1.0
Cincinnati,1.0
Cincinnati,0.0
Cincinnati,0.0
Cincinnati,0.0
Cincinnati,0.0
Cincinnati,0.0
Dallas,1.0
Dallas,1.0
Dallas,0.0
Dallas,1.0
Dallas,2.0
Dallas,0.0
Dallas,0.0
Dallas,0.0
Dallas,4.0
Dallas,6.0
Dallas,15.0
Dallas,0.0
Dallas,5.0
Dallas,15.0
Dallas,13.0
Dallas,0.0
Dallas,9.0
Dallas,0.0
Dallas,0.0
Dallas,0.0
Dallas,1.0
Dallas,8.0
Dallas,5.0
Dallas,9.0
Dallas,2.0
Dallas,7.0
Dallas,7.0
Dallas,3.0
Dallas,3.0
Dallas,2.0
Dallas,0.0
Dallas,1.0
Dallas,13.0
Dallas,3.0
Dallas,7.0
Dallas,8.0
Dallas,8.0
Dallas,5.0
Dallas,4.0
Dallas,1.0
Denver,2.0
Denver,0.0
Denver,0.0
Denver,0.0
Denver,2.0
Denver,0.0
Denver,0.0
Denver,0.0
Denver,4.0
Denver,1.0
Denver,4.0
Denver,0.0
Denver,0.0
Denver,0.0
Denver,3.0
Denver,0.0
Denver,5.0
Denver,8.0
Denver,11.0
Denver,5.0
Denver,2.0
Denver,5.0
Denver,1.0
Denver,3.0
Denver,1.0
Denver,1.0
Denver,7.0
Denver,6.0
Denver,6.0
Denver,1.0
Denver,4.0
Denver,7.0
Denver,3.0
Denver,2.0
Denver,0.0
Denver,4.0
Denver,3.0
Denver,3.0
Denver,1.0
Denver,0.0
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Detroit,
Green Bay,8.0
Green Bay,0.0
Green Bay,3.0
Green Bay,6.0
Green Bay,1.0
Green Bay,4.0
Green Bay,21.0
Green Bay,3.0
Green Bay,4.0
Green Bay,15.0
Green Bay,3.0
Green Bay,1.0
Green Bay,9.0
Green Bay,2.0
Green Bay,18.0
Green Bay,9.0
Green Bay,1.0
Green Bay,8.0
Green Bay,6.0
Green Bay,13.0
Green Bay,6.0
Green Bay,8.0
Green Bay,7.0
Green Bay,16.0
Green Bay,8.0
Green Bay,4.0
Green Bay,1.0
Green Bay,15.0
Green Bay,3.0
Green Bay,8.0
Green Bay,11.0
Green Bay,6.0
Green Bay,13.0
Green Bay,4.0
Green Bay,4.0
Green Bay,13.0
Green Bay,8.0
Green Bay,2.0
Green Bay,5.0
Green Bay,11.0
Houston,2.0
Houston,2.0
Houston,1.0
Houston,0.0
Houston,0.0
Houston,2.0
Houston,2.0
Houston,0.0
Houston,6.0
Houston,1.0
Houston,4.0
Houston,1.0
Houston,1.0
Houston,1.0
Houston,1.0
Houston,3.0
Houston,2.0
Houston,0.0
Houston,1.0
Houston,1.0
Houston,2.0
Houston,1.0
Houston,0.0
Houston,0.0
Houston,1.0
Houston,0.0
Houston,0.0
Houston,0.0
Houston,1.0
Houston,0.0
Houston,0.0
Houston,0.0
Houston,1.0
Houston,1.0
Houston,0.0
Houston,0.0
Houston,1.0
Houston,1.0
Houston,0.0
Houston,0.0
Indianapolis,2.0
Indianapolis,11.0
Indianapolis,0.0
Indianapolis,3.0
Indianapolis,0.0
Indianapolis,7.0
Indianapolis,0.0
Indianapolis,2.0
Indianapolis,1.0
Indianapolis,0.0
Indianapolis,3.0
Indianapolis,2.0
Indianapolis,4.0
Indianapolis,5.0
Indianapolis,1.0
Indianapolis,0.0
Indianapolis,0.0
Indianapolis,4.0
Indianapolis,2.0
Indianapolis,10.0
Indianapolis,1.0
Indianapolis,3.0
Indianapolis,0.0
Indianapolis,2.0
Indianapolis,4.0
Indianapolis,0.0
Indianapolis,0.0
Indianapolis,5.0
Indianapolis,3.0
Indianapolis,1.0
Indianapolis,4.0
Indianapolis,0.0
Indianapolis,3.0
Indianapolis,0.0
Indianapolis,2.0
Indianapolis,0.0
Indianapolis,2.0
Indianapolis,0.0
Indianapolis,3.0
Indianapolis,1.0
Jacksonville,4.0
Jacksonville,4.0
Jacksonville,2.0
Jacksonville,3.0
Jacksonville,1.0
Jacksonville,6.0
Jacksonville,3.0
Jacksonville,1.0
Jacksonville,5.0
Jacksonville,1.0
Jacksonville,2.0
Jacksonville,0.0
Jacksonville,0.0
Jacksonville,0.0
Jacksonville,1.0
Jacksonville,1.0
Jacksonville,3.0
Jacksonville,0.0
Jacksonville,2.0
Jacksonville,3.0
Jacksonville,3.0
Jacksonville,0.0
Jacksonville,1.0
Jacksonville,3.0
Jacksonville,3.0
Jacksonville,0.0
Jacksonville,3.0
Jacksonville,0.0
Jacksonville,0.0
Jacksonville,1.0
Jacksonville,1.0
Jacksonville,2.0
Jacksonville,0.0
Jacksonville,1.0
Jacksonville,0.0
Jacksonville,2.0
Jacksonville,2.0
Kansas City,0.0
Kansas City,1.0
Kansas City,2.0
Kansas City,3.0
Kansas City,1.0
Kansas City,0.0
Kansas City,2.0
Kansas City,2.0
Kansas City,0.0
Kansas City,0.0
Kansas City,0.0
Kansas City,4.0
Kansas City,0.0
Kansas City,1.0
Kansas City,1.0
Kansas City,0.0
Kansas City,4.0
Kansas City,4.0
Kansas City,0.0
Kansas City,2.0
Kansas City,3.0
Kansas City,4.0
Kansas City,4.0
Kansas City,0.0
Kansas City,1.0
Kansas City,5.0
Kansas City,2.0
Kansas City,1.0
Kansas City,2.0
Kansas City,5.0
Kansas City,8.0
Kansas City,3.0
Kansas City,2.0
Kansas City,0.0
Kansas City,1.0
Kansas City,0.0
Kansas City,1.0
Kansas City,0.0
Kansas City,2.0
Miami,1.0
Miami,4.0
Miami,0.0
Miami,3.0
Miami,0.0
Miami,4.0
Miami,2.0
Miami,5.0
Miami,3.0
Miami,0.0
Miami,0.0
Miami,0.0
Miami,4.0
Miami,3.0
Miami,1.0
Miami,3.0
Miami,2.0
Miami,4.0
Miami,4.0
Miami,5.0
Miami,4.0
Miami,1.0
Miami,2.0
Miami,7.0
Miami,5.0
Miami,1.0
Miami,1.0
Miami,2.0
Miami,2.0
Miami,0.0
Miami,1.0
New England,4.0
New England,6.0
New England,7.0
New England,2.0
New England,12.0
New England,6.0
New England,3.0
New England,1.0
New England,9.0
New England,6.0
New England,4.0
New England,5.0
New England,3.0
New England,7.0
New England,7.0
New England,2.0
New England,14.0
New England,1.0
New England,6.0
New England,1.0
New England,2.0
New England,4.0
New England,5.0
New England,4.0
New England,7.0
New England,7.0
New England,7.0
New England,6.0
New England,1.0
New England,2.0
New England,6.0
New England,2.0
New England,4.0
New England,0.0
New England,3.0
New England,6.0
New England,2.0
New England,9.0
New England,3.0
New England,2.0
New York Giants,18.0
New York Giants,15.0
New York Giants,19.0
New York Giants,23.0
New York Giants,26.0
New York Giants,35.0
New York Giants,31.0
New York Giants,21.0
New York Giants,39.0
New York Giants,6.0
New York Giants,12.0
New York Giants,16.0
New York Giants,20.0
New York Giants,23.0
New York Giants,14.0
New York Giants,15.0
New York Giants,21.0
New York Giants,12.0
New York Giants,19.0
New York Giants,29.0
New York Giants,16.0
New York Giants,46.0
New York Giants,29.0
New York Giants,10.0
New York Giants,16.0
New York Giants,22.0
New York Giants,24.0
New York Giants,20.0
New York Giants,23.0
New York Giants,33.0
New York Giants,9.0
New York Giants,28.0
New York Giants,18.0
New York Giants,24.0
New York Giants,26.0
New York Giants,35.0
New York Giants,22.0
New York Giants,39.0
New York Giants,31.0
New York Giants,14.0
New York Jets,34.0
New York Jets,23.0
New York Jets,28.0
New York Jets,20.0
New York Jets,30.0
New York Jets,12.0
New York Jets,14.0
New York Jets,31.0
New York Jets,22.0
New York Jets,18.0
New York Jets,15.0
New York Jets,10.0
New York Jets,16.0
New York Jets,38.0
New York Jets,11.0
New York Jets,18.0
New York Jets,17.0
New York Jets,22.0
New York Jets,20.0
New York Jets,29.0
New York Jets,11.0
New York Jets,26.0
New York Jets,8.0
New York Jets,10.0
New York Jets,12.0
New York Jets,27.0
New York Jets,22.0
New York Jets,18.0
New York Jets,25.0
New York Jets,14.0
New York Jets,20.0
New York Jets,28.0
New York Jets,7.0
New York Jets,26.0
New York Jets,28.0
New York Jets,15.0
New York Jets,44.0
New York Jets,27.0
New York Jets,30.0
New York Jets,32.0
Oakland,12.0
Oakland,15.0
Oakland,7.0
Oakland,12.0
Oakland,28.0
Oakland,15.0
Oakland,19.0
Oakland,19.0
Oakland,17.0
Oakland,25.0
Oakland,16.0
Oakland,17.0
Oakland,19.0
Oakland,7.0
Oakland,24.0
Oakland,8.0
Oakland,10.0
Oakland,15.0
Oakland,20.0
Oakland,14.0
Oakland,13.0
Oakland,20.0
Oakland,21.0
Oakland,10.0
Oakland,18.0
Oakland,30.0
Oakland,25.0
Oakland,49.0
Oakland,21.0
Oakland,11.0
Oakland,18.0
Oakland,21.0
Oakland,16.0
Oakland,22.0
Oakland,19.0
Oakland,15.0
Oakland,10.0
Philadelphia,2.0
Philadelphia,5.0
Philadelphia,5.0
Philadelphia,2.0
Philadelphia,2.0
Philadelphia,2.0
Philadelphia,1.0
Philadelphia,2.0
Philadelphia,2.0
Philadelphia,6.0
Philadelphia,1.0
Philadelphia,0.0
Philadelphia,4.0
Philadelphia,1.0
Philadelphia,1.0
Philadelphia,1.0
Philadelphia,2.0
Philadelphia,2.0
Philadelphia,1.0
Philadelphia,18.0
Philadelphia,3.0
Philadelphia,3.0
Philadelphia,10.0
Philadelphia,12.0
Philadelphia,3.0
Philadelphia,3.0
Philadelphia,1.0
Philadelphia,1.0
Philadelphia,1.0
Philadelphia,5.0
Philadelphia,2.0
Philadelphia,4.0
Philadelphia,5.0
Philadelphia,0.0
Philadelphia,2.0
Philadelphia,2.0
Philadelphia,0.0
Philadelphia,1.0
Philadelphia,5.0
Philadelphia,3.0
Pittsburgh,15.0
Pittsburgh,19.0
Pittsburgh,24.0
Pittsburgh,12.0
Pittsburgh,21.0
Pittsburgh,9.0
Pittsburgh,16.0
Pittsburgh,10.0
Pittsburgh,25.0
Pittsburgh,18.0
Pittsburgh,23.0
Pittsburgh,25.0
Pittsburgh,52.0
Pittsburgh,31.0
Pittsbu

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1 Reply

0 votes
by (71.8m points)

copying data from your link and running df = pd.read_clipboard()

Plot using pandas.DataFrame.plot

Updated to pandas v1.2.4 and matplotlib v3.3.4

then using your code

df = df.replace(np.nan, 0)
dfg = df.groupby(['home_team'])['arrests'].mean()

dfg.plot(kind='bar', title='Arrests', ylabel='Mean Arrests',
         xlabel='Home Team', figsize=(6, 5))

enter image description here


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