Histogram of dataframe on column groupby-CodePudding

Suppose I have the following df:

df
   user      start                end           mode
0   82  2009-04-19 05:49:50 2009-04-19 06:17:40 metro
1   82  2009-04-19 06:18:05 2009-04-19 06:22:44 foot
2   10  2007-06-26 11:32:29 2007-06-26 11:40:29 bus
3   10  2008-03-28 14:52:54 2008-03-28 15:59:59 metro
4   20  2011-08-27 06:13:01 2011-08-27 08:01:37 foot
5   20  2012-02-20 14:10:33 2012-02-20 14:29:59 bus
6   20  2012-02-21 01:22:05 2012-02-21 01:55:47 bus

So I want group my df by mode column and plot histogram for (year, month) showing how many of each mode are there in that month of that year.

So I do:

df[['mode']].groupby([df['start'].dt.year, df['start'].dt.month]).count().plot(kind='barh')

output:

The result is cumulative value of mode for each month.

But I want to see the number of each mode (where available) in every month of each year.

CodePudding user response：

You could use seaborn and pass the hue paramter.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'user': [82, 82, 10, 10, 20, 20, 20],
 'start': ['2009-04-19 05:49:50',
  '2009-04-19 06:18:05',
  '2007-06-26 11:32:29',
  '2008-03-28 14:52:54',
  '2011-08-27 06:13:01',
  '2012-02-20 14:10:33',
  '2012-02-21 01:22:05'],
 'end': ['2009-04-19 06:17:40',
  '2009-04-19 06:22:44',
  '2007-06-26 11:40:29',
  '2008-03-28 15:59:59',
  '2011-08-27 08:01:37',
  '2012-02-20 14:29:59',
  '2012-02-21 01:55:47'],
 'mode': ['metro', 'foot', 'bus', 'metro', 'foot', 'bus', 'bus']})


df['start'] = pd.to_datetime(df['start'])
df['end'] = pd.to_datetime(df['end'])


df['year'] = df.start.dt.year
df['month'] = df.start.dt.month
df = df.groupby(['year','month','mode']).size().reset_index().rename(columns={0:'count'})
g = sns.barplot(data=df, y=df['year'].astype(str) ',' df['month'].astype(str),x='count', hue='mode', orient='h')
plt.ylim(reversed(plt.ylim()));