Plotting boxplot with pandas dataframe

Question

I've created a pandas dataframe of PM2.5 data and want to create individual boxplots for each site with box and whisker plots for each year within this (with the top and bottom 10th percentiles). How do I go about this?

Date            Aberdeen  Auchencorth Moss  Belfast Centre  Birmingham Tyburn  

2000-01-01        NaN               NaN             NaN                NaN   
2000-01-02        NaN               NaN             NaN                NaN   
2000-01-03        NaN               NaN             NaN                NaN   
2000-01-04        NaN               NaN             NaN                NaN   
2000-01-05        NaN               NaN             NaN                NaN   
2000-01-06        NaN               NaN             NaN                NaN   
2000-01-07        NaN               NaN             NaN                NaN   
2000-01-08        NaN               NaN             NaN                NaN   
2000-01-09        NaN               NaN             NaN                NaN   
2000-01-10        NaN               NaN             NaN                NaN   
2000-01-11        NaN               NaN             NaN                NaN   
2000-01-12        NaN               NaN             NaN                NaN   
2000-01-13        NaN               NaN             NaN                NaN   
2000-01-14        NaN               NaN             NaN                NaN   
2000-01-15        NaN               NaN             NaN                NaN   
2000-01-16        NaN               NaN             NaN                NaN   
2000-01-17        NaN               NaN             NaN                NaN   
2000-01-18        NaN               NaN             NaN                NaN   
2000-01-19        NaN               NaN             NaN                NaN   
2000-01-20        NaN               NaN             NaN                NaN   
2000-01-21        NaN               NaN             NaN                NaN   
2000-01-22        NaN               NaN             NaN                NaN   
2000-01-23        NaN               NaN             NaN                NaN   
2000-01-24        NaN               NaN             NaN                NaN   
2000-01-25        NaN               NaN             NaN                NaN   
2000-01-26        NaN               NaN             NaN                NaN   
2000-01-27        NaN               NaN             NaN                NaN   
2000-01-28        NaN               NaN             NaN                NaN   
2000-01-29        NaN               NaN             NaN                NaN   
2000-01-30        NaN               NaN             NaN                NaN   
              ...               ...             ...                ...   
2017-04-02        3.0               4.0             7.0               10.0   
2017-04-03        5.0               4.0             9.0               14.0   
2017-04-04        3.0               5.0             8.0                9.0   
2017-04-05        7.0               5.0             7.0                7.0   
2017-04-06        3.0               3.0             7.0               10.0   
2017-04-07        3.0               3.0            11.0               14.0   
2017-04-08       11.0              12.0            20.0               26.0   
2017-04-09       11.0              15.0            17.0               25.0   
2017-04-10        3.0               4.0             8.0                5.0   
2017-04-11        1.0               6.0             9.0                7.0   
2017-04-12        2.0               4.0             5.0                6.0   
2017-04-13        2.0               3.0             6.0                6.0   
2017-04-14        2.0               3.0             6.0                6.0   
2017-04-15        3.0               3.0             6.0                6.0   
2017-04-16        3.0               3.0             5.0                5.0   
2017-04-17        4.0               3.0             7.0               11.0   
2017-04-18        4.0               3.0             7.0                7.0   
2017-04-19        6.0               4.0            11.0               13.0   
2017-04-20        3.0               4.0            12.0               12.0   
2017-04-21        3.0               4.0            11.0               11.0   
2017-04-22        3.0               4.0             9.0                8.0   
2017-04-23        3.0               4.0             6.0                9.0   
2017-04-24        3.0               2.0             4.0                6.0   
2017-04-25        3.0               3.0             6.0                5.0   
2017-04-26        3.0               3.0             6.0                6.0   
2017-04-27        3.0               2.0             6.0                8.0   
2017-04-28        NaN               3.0             8.0                8.0   
2017-04-29        NaN               6.0             7.0                9.0   
2017-04-30        NaN              17.0            20.0               19.0   
2017-05-01       19.0              18.0            20.0                8.0

Ukrainian-serge · Accepted Answer · 2020-03-07 19:04:25Z

2

Here's a go.

import matplotlib.pyplot as plt
import pandas as pd

# copy your sample
df = pd.read_clipboard(header=0, index_col='Date').fillna(0)

# remove row with '...'
df = df[df.Moss != '...'].astype(float)

# set index to datetime index
df.index = pd.DatetimeIndex(df.index)

# groupby year
grouped = dict(list(df.groupby(date_index.year)))

# set up a figure with 1 row, 2 colums
fig, ax = plt.subplots(1, 2,
                       sharey=True, 
                       figsize=(8, 6), 
                       tight_layout=True)

# iterate through our grouped and plot 
for i, (k, v) in enumerate(grouped.items()):
    v.boxplot(ax=ax[i],
              rot=90, 
              figsize=(3, 3)).set(title=f'{k}')

plt.show()

Year 2000 has no data points, but you can see the gist of it.

edited Mar 7, 2020 at 19:04

answered Mar 7, 2020 at 8:32

Ukrainian-serge

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Comments

Diego Aguado · Accepted Answer · 2017-07-12 19:26:41Z

1

You could try using matplotlib

import matplotlib.pyplot as plt
plt.boxplot(your_df['Aberdeen'].values)
plt.show()

If you are using jupyter use %matplotlib inline

answered Jul 12, 2017 at 19:26

Diego Aguado

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Comments

Karan Arya · Accepted Answer · 2019-08-15 18:34:41Z

0

import pandas as pd
df = pd.DataFrame({'Date': ['2000-10-10', '2000-09-20', '2001-09-23', '2001-09-10', '2001-09-02', '2002-04-29',],
          'Aberdeen': [9,5,1, 2, 6, 1]})

df['Year'] = df['Date'].str.split('-')
df['Year'] = df['Year'].apply(lambda x : x[0])
sns.boxplot(df['Year'], df['Aberdeen'])

answered Aug 15, 2019 at 18:34

Karan Arya

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