CODEWITHSUNDEEP
pythonpandas
Richard
Richard

Reputation: 65530

How to create hierarchical columns in pandas?

I have a pandas dataframe that looks like this:

          rank_2015   num_2015   rank_2014   num_2014   ....  num_2008
France    8           1200       9           1216       ....  1171
Italy     11          789        6           788        ....  654

Now I want to draw a bar chart of the sums just the num_ columns, by year. So on the x-axis I would like years from 2008 to 2015, and on the y-axis I would like the sum of the related num_ column.

What's the best way to do this? I know how to get the sums for each column:

df.sum()

But what I don't know is how to chart only the num_ columns, and also how to re-label those columns so that the labels are integers rather than strings, in order to get them to chart correctly.

I'm wondering if I want to create hierarchical columns, like this:

          rank               num
          2015        2014   2015     2014   ....  2008
France    8           9      1200     1216   ....  1171
Italy     11          6      789      788    ....  654

Then I could just chart the columns in the num section.

How can I get my dataframe into this shape?

Upvotes: 4

Views: 6646

Answers (2)

unutbu
unutbu

Reputation: 879591

You could use str.extract with the regex pattern (.+)_(\d+) to convert the columns to a DataFrame:

cols = df.columns.str.extract(r'(.+)_(\d+)', expand=True)
#       0     1
# 0   num  2008
# 1   num  2014
# 2   num  2015
# 3  rank  2014
# 4  rank  2015

You can then build a hierarchical (MultiIndex) index from cols and reassign it to df.columns:

df.columns = pd.MultiIndex.from_arrays((cols[0], cols[1]))

so that df becomes

         num             rank     
        2008  2014  2015 2014 2015
France  1171  1216  1200    9    8
Italy    654   788   789    6   11

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({ 'num_2008': [1171, 654],
 'num_2014': [1216, 788],
 'num_2015': [1200, 789],
 'rank_2014': [9, 6],
 'rank_2015': [8, 11]}, index=['France', 'Italy'])


cols = df.columns.str.extract(r'(.+)_(\d+)', expand=True)
cols[1] = pd.to_numeric(cols[1])
df.columns = pd.MultiIndex.from_arrays((cols[0], cols[1]))
df.columns.names = [None]*2

df['num'].sum().plot(kind='bar')
plt.show()

enter image description here

Upvotes: 7

jackdaw
jackdaw

Reputation: 564

Probably you don't need re-shaping your dataset, it can be achieved easier.

  1. Create new dataset, which contains num_ data only
  2. Rename columns
  3. Plot sum

Dummy data:

enter image description here

Code:

df_num = df[[c for c in df.columns if c.startswith('num_')]]
df_num.columns = [c.lstrip('num_') for c in df_num.columns]
df_num.sum().plot(kind='bar')

Result:

enter image description here

Upvotes: 1

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