How to spread a column in a Pandas data frame

Question

I have the following pandas data frame:

import pandas as pd
import numpy as np
df = pd.DataFrame({
               'fc': [100,100,112,1.3,14,125],
               'sample_id': ['S1','S1','S1','S2','S2','S2'],
               'gene_symbol': ['a', 'b', 'c', 'a', 'b', 'c'],
               })

df = df[['gene_symbol', 'sample_id', 'fc']]
df

Which produces this:

Out[11]:
  gene_symbol sample_id     fc
0           a        S1  100.0
1           b        S1  100.0
2           c        S1  112.0
3           a        S2    1.3
4           b        S2   14.0
5           c        S2  125.0

How can I spread sample_id so that in the end I get this:

gene_symbol    S1   S2
a             100   1.3
b             100   14.0
c             112   125.0

jezrael · Accepted Answer · 2017-06-23 08:01:28Z

27

Use pivot or unstack:

#df = df[['gene_symbol', 'sample_id', 'fc']]
df = df.pivot(index='gene_symbol',columns='sample_id',values='fc')
print (df)
sample_id       S1     S2
gene_symbol              
a            100.0    1.3
b            100.0   14.0
c            112.0  125.0

df = df.set_index(['gene_symbol','sample_id'])['fc'].unstack(fill_value=0)
print (df)
sample_id       S1     S2
gene_symbol              
a            100.0    1.3
b            100.0   14.0
c            112.0  125.0

But if duplicates, need pivot_table or aggregate with groupby or , mean can be changed to sum, median, ...:

df = pd.DataFrame({
               'fc': [100,100,112,1.3,14,125, 100],
               'sample_id': ['S1','S1','S1','S2','S2','S2', 'S2'],
               'gene_symbol': ['a', 'b', 'c', 'a', 'b', 'c', 'c'],
               })
print (df)
      fc gene_symbol sample_id
0  100.0           a        S1
1  100.0           b        S1
2  112.0           c        S1
3    1.3           a        S2
4   14.0           b        S2
5  125.0           c        S2 <- same c, S2, different fc
6  100.0           c        S2 <- same c, S2, different fc

df = df.pivot(index='gene_symbol',columns='sample_id',values='fc')

ValueError: Index contains duplicate entries, cannot reshape

df = df.pivot_table(index='gene_symbol',columns='sample_id',values='fc', aggfunc='mean')
print (df)
sample_id       S1     S2
gene_symbol              
a            100.0    1.3
b            100.0   14.0
c            112.0  112.5

df = df.groupby(['gene_symbol','sample_id'])['fc'].mean().unstack(fill_value=0)
print (df)
sample_id       S1     S2
gene_symbol              
a            100.0    1.3
b            100.0   14.0
c            112.0  112.5

EDIT:

For cleaning set columns name to None and reset_index:

df.columns.name = None
df = df.reset_index()
print (df)
  gene_symbol     S1     S2
0           a  100.0    1.3
1           b  100.0   14.0
2           c  112.0  112.5

edited Jun 23, 2017 at 8:01

answered May 15, 2017 at 7:50

jezrael

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4 Comments

neversaint Over a year ago

Thanks a million. How can I flatten the index. So I get exactly the result as in OP?

jezrael Over a year ago

Give me as sec.

jezrael Over a year ago

Do you need gene_symbol as index or as column?

neversaint Over a year ago

as column. And don't want sample_id index to appear.

MaxU - stand with Ukraine · Accepted Answer · 2017-05-15 13:39:27Z

3

you can also use pd.crosstab() method:

In [82]: pd.crosstab(index=df.gene_symbol, columns=df.sample_id, 
                     values=df.fc, aggfunc='mean') \
    ...:   .rename_axis(None,1) \
    ...:   .reset_index()
    ...:
Out[82]:
  gene_symbol     S1     S2
0           a  100.0    1.3
1           b  100.0   14.0
2           c  112.0  125.0

answered May 15, 2017 at 13:39

MaxU - stand with Ukraine

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