How can I pivot a dataframe?

Question

What is pivot?
How do I pivot?
Long format to wide format?

I've seen a lot of questions that ask about pivot tables, even if they don't know it. It is virtually impossible to write a [canonical question and answer](https://meta.stackoverflow.com/questions/291992/what-is-a-canonical- question-answer-and-what-is-their-purpose) that encompasses all aspects of pivoting... But I'm going to give it a go.

The problem with existing questions and answers is that often the question is focused on a nuance that the OP has trouble generalizing in order to use a number of the existing good answers. However, none of the answers attempt to give a comprehensive explanation (because it's a daunting task). Look at a few examples from my Google search:

How to pivot a dataframe in Pandas? - Good question and answer. But the answer only answers the specific question with little explanation.
pandas pivot table to data frame - OP is concerned with the output of the pivot, namely how the columns look. OP wanted it to look like R. This isn't very helpful for pandas users.
pandas pivoting a dataframe, duplicate rows - Another decent question but the answer focuses on one method, namely pd.DataFrame.pivot

Setup

I conspicuously named my columns and relevant column values to correspond with how I'm going to pivot in the answers below.

import numpy as np
import pandas as pd
from numpy.core.defchararray import add

np.random.seed([3,1415])
n = 20

cols = np.array(['key', 'row', 'item', 'col'])
arr1 = (np.random.randint(5, size=(n, 4)) // [2, 1, 2, 1]).astype(str)

df = pd.DataFrame(
    add(cols, arr1), columns=cols
).join(
    pd.DataFrame(np.random.rand(n, 2).round(2)).add_prefix('val')
)
print(df)



     key   row   item   col  val0  val1
0   key0  row3  item1  col3  0.81  0.04
1   key1  row2  item1  col2  0.44  0.07
2   key1  row0  item1  col0  0.77  0.01
3   key0  row4  item0  col2  0.15  0.59
4   key1  row0  item2  col1  0.81  0.64
5   key1  row2  item2  col4  0.13  0.88
6   key2  row4  item1  col3  0.88  0.39
7   key1  row4  item1  col1  0.10  0.07
8   key1  row0  item2  col4  0.65  0.02
9   key1  row2  item0  col2  0.35  0.61
10  key2  row0  item2  col1  0.40  0.85
11  key2  row4  item1  col2  0.64  0.25
12  key0  row2  item2  col3  0.50  0.44
13  key0  row4  item1  col4  0.24  0.46
14  key1  row3  item2  col3  0.28  0.11
15  key0  row3  item1  col1  0.31  0.23
16  key0  row0  item2  col3  0.86  0.01
17  key0  row4  item0  col3  0.64  0.21
18  key2  row2  item2  col0  0.13  0.45
19  key0  row2  item0  col4  0.37  0.70

Questions

Why do I get ValueError: Index contains duplicate entries, cannot reshape?
How do I pivot df such that the col values are columns, row values are the index, and mean of val0 are the values?

col col0 col1 col2 col3 col4 row row0 0.77 0.605 NaN 0.860 0.65 row2 0.13 NaN 0.395 0.500 0.25 row3 NaN 0.310 NaN 0.545 NaN row4 NaN 0.100 0.395 0.760 0.24
How do I make it so that missing values are 0?

col col0 col1 col2 col3 col4 row row0 0.77 0.605 0.000 0.860 0.65 row2 0.13 0.000 0.395 0.500 0.25 row3 0.00 0.310 0.000 0.545 0.00 row4 0.00 0.100 0.395 0.760 0.24
Can I get something other than mean, like maybe sum?

col col0 col1 col2 col3 col4 row row0 0.77 1.21 0.00 0.86 0.65 row2 0.13 0.00 0.79 0.50 0.50 row3 0.00 0.31 0.00 1.09 0.00 row4 0.00 0.10 0.79 1.52 0.24

Can I do more that one aggregation at a time?

      sum                          mean

col   col0  col1  col2  col3  col4  col0   col1   col2   col3  col4
row
row0  0.77  1.21  0.00  0.86  0.65  0.77  0.605  0.000  0.860  0.65
row2  0.13  0.00  0.79  0.50  0.50  0.13  0.000  0.395  0.500  0.25
row3  0.00  0.31  0.00  1.09  0.00  0.00  0.310  0.000  0.545  0.00
row4  0.00  0.10  0.79  1.52  0.24  0.00  0.100  0.395  0.760  0.24

Can I aggregate over multiple value columns?

     val0                             val1

col   col0   col1   col2   col3  col4  col0   col1  col2   col3  col4
row
row0  0.77  0.605  0.000  0.860  0.65  0.01  0.745  0.00  0.010  0.02
row2  0.13  0.000  0.395  0.500  0.25  0.45  0.000  0.34  0.440  0.79
row3  0.00  0.310  0.000  0.545  0.00  0.00  0.230  0.00  0.075  0.00
row4  0.00  0.100  0.395  0.760  0.24  0.00  0.070  0.42  0.300  0.46

Can I subdivide by multiple columns?

item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 row row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.605 0.86 0.65 row2 0.35 0.00 0.37 0.00 0.00 0.44 0.00 0.00 0.13 0.000 0.50 0.13 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.000 0.28 0.00 row4 0.15 0.64 0.00 0.00 0.10 0.64 0.88 0.24 0.00 0.000 0.00 0.00
Or

item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 key row key0 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.86 0.00 row2 0.00 0.00 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.00 0.00 0.00 row4 0.15 0.64 0.00 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 key1 row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.81 0.00 0.65 row2 0.35 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.13 row3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 row4 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 key2 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.00 0.00 row2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 row4 0.00 0.00 0.00 0.00 0.00 0.64 0.88 0.00 0.00 0.00 0.00 0.00
Can I aggregate the frequency in which the column and rows occur together, aka "cross tabulation"?

col col0 col1 col2 col3 col4 row row0 1 2 0 1 1 row2 1 0 2 1 2 row3 0 1 0 2 0 row4 0 1 2 2 1
How do I convert a DataFrame from long to wide by pivoting on ONLY two columns? Given,

np.random.seed([3, 1415]) df2 = pd.DataFrame({'A': list('aaaabbbc'), 'B': np.random.choice(15, 8)}) df2 A B 0 a 0 1 a 11 2 a 2 3 a 11 4 b 10 5 b 10 6 b 14 7 c 7

The expected should look something like

          a     b    c
0   0.0  10.0  7.0
1  11.0  10.0  NaN
2   2.0  14.0  NaN
3  11.0   NaN  NaN

How do I flatten the multiple index to single index after pivot?

From

       1|1  2|1  2|2
a    2    1    1
b    2    1    0
c    1    0    0

Answer

Here is a list of idioms we can use to pivot

pd.DataFrame.pivot_table

A glorified version of groupby with more intuitive API. For many people, this is the preferred approach. And it is the intended approach by the developers.
Specify row level, column levels, values to be aggregated, and function(s) to perform aggregations.

pd.DataFrame.groupby + pd.DataFrame.unstack

Good general approach for doing just about any type of pivot
You specify all columns that will constitute the pivoted row levels and column levels in one group by. You follow that by selecting the remaining columns you want to aggregate and the function(s) you want to perform the aggregation. Finally, you unstack the levels that you want to be in the column index.

pd.DataFrame.set_index + pd.DataFrame.unstack

Convenient and intuitive for some (myself included). Cannot handle duplicate grouped keys.
Similar to the groupby paradigm, we specify all columns that will eventually be either row or column levels and set those to be the index. We then unstack the levels we want in the columns. If either the remaining index levels or column levels are not unique, this method will fail.

pd.DataFrame.pivot

Very similar to set_index in that it shares the duplicate key limitation. The API is very limited as well. It only takes scalar values for index, columns, values.
Similar to the pivot_table method in that we select rows, columns, and values on which to pivot. However, we cannot aggregate and if either rows or columns are not unique, this method will fail.

pd.crosstab

This a specialized version of pivot_table and in its purest form is the most intuitive way to perform several tasks.

pd.factorize + np.bincount

This is a highly advanced technique that is very obscure but is very fast. It cannot be used in all circumstances, but when it can be used and you are comfortable using it, you will reap the performance rewards.

pd.get_dummies + pd.DataFrame.dot

I use this for cleverly performing cross tabulation.

Question 1

Why do I get ValueError: Index contains duplicate entries, cannot reshape

This occurs because pandas is attempting to reindex either a columns or index object with duplicate entries. There are varying methods to use that can perform a pivot. Some of them are not well suited to when there are duplicates of the keys on which it is being asked to pivot. For example: Consider pd.DataFrame.pivot. I know there are duplicate entries that share the row and col values:

df.duplicated(['row', 'col']).any()

True

So when I pivot using

df.pivot(index='row', columns='col', values='val0')

I get the error mentioned above. In fact, I get the same error when I try to perform the same task with:

df.set_index(['row', 'col'])['val0'].unstack()

Examples

What I'm going to do for each subsequent question is to answer it using [pd.DataFrame.pivot_table](https://pandas.pydata.org/pandas- docs/stable/reference/api/pandas.DataFrame.pivot_table.html). Then I'll provide alternatives to perform the same task.

Questions 2 and 3

How do I pivot df such that the col values are columns, row values are the index, and mean of val0 are the values?

pd.DataFrame.pivot_table
```
df.pivot_table(
values='val0', index='row', columns='col',
aggfunc='mean')
```
col col0 col1 col2 col3 col4 row
row0 0.77 0.605 NaN 0.860 0.65 row2 0.13 NaN 0.395 0.500 0.25 row3 NaN 0.310 NaN 0.545 NaN row4 NaN 0.100 0.395 0.760 0.24
- aggfunc='mean' is the default and I didn't have to set it. I included it to be explicit.

How do I make it so that missing values are 0?

pd.DataFrame.pivot_table
- fill_value is not set by default. I tend to set it appropriately. In this case I set it to 0.
  
  df.pivot_table( values='val0', index='row', columns='col', fill_value=0, aggfunc='mean')
col col0 col1 col2 col3 col4 row row0 0.77 0.605 0.000 0.860 0.65 row2 0.13 0.000 0.395 0.500 0.25 row3 0.00 0.310 0.000 0.545 0.00 row4 0.00 0.100 0.395 0.760 0.24

pd.DataFrame.groupby

df.groupby(['row', 'col'])['val0'].mean().unstack(fill_value=0)

pd.crosstab

pd.crosstab(
index=df['row'], columns=df['col'],
values=df['val0'], aggfunc='mean').fillna(0)

Question 4

Can I get something other than mean, like maybe sum?

pd.DataFrame.pivot_table
```
df.pivot_table(
values='val0', index='row', columns='col',
fill_value=0, aggfunc='sum')
```
col col0 col1 col2 col3 col4 row row0 0.77 1.21 0.00 0.86 0.65 row2 0.13 0.00 0.79 0.50 0.50 row3 0.00 0.31 0.00 1.09 0.00 row4 0.00 0.10 0.79 1.52 0.24

pd.DataFrame.groupby

df.groupby(['row', 'col'])['val0'].sum().unstack(fill_value=0)

pd.crosstab

pd.crosstab(
index=df['row'], columns=df['col'],
values=df['val0'], aggfunc='sum').fillna(0)

Question 5

Can I do more that one aggregation at a time?

Notice that for pivot_table and crosstab I needed to pass list of callables. On the other hand, groupby.agg is able to take strings for a limited number of special functions. groupby.agg would also have taken the same callables we passed to the others, but it is often more efficient to leverage the string function names as there are efficiencies to be gained.

pd.DataFrame.pivot_table
```
df.pivot_table(
values='val0', index='row', columns='col',
fill_value=0, aggfunc=[np.size, np.mean])

 size                      mean
```
col col0 col1 col2 col3 col4 col0 col1 col2 col3 col4 row row0 1 2 0 1 1 0.77 0.605 0.000 0.860 0.65 row2 1 0 2 1 2 0.13 0.000 0.395 0.500 0.25 row3 0 1 0 2 0 0.00 0.310 0.000 0.545 0.00 row4 0 1 2 2 1 0.00 0.100 0.395 0.760 0.24

pd.DataFrame.groupby

df.groupby(['row', 'col'])['val0'].agg(['size', 'mean']).unstack(fill_value=0)

pd.crosstab

pd.crosstab(
index=df['row'], columns=df['col'],
values=df['val0'], aggfunc=[np.size, np.mean]).fillna(0, downcast='infer')

Question 6

Can I aggregate over multiple value columns?

pd.DataFrame.pivot_table we pass values=['val0', 'val1'] but we could've left that off completely
```
df.pivot_table(
values=['val0', 'val1'], index='row', columns='col',
fill_value=0, aggfunc='mean')

  val0                             val1
```
col col0 col1 col2 col3 col4 col0 col1 col2 col3 col4 row row0 0.77 0.605 0.000 0.860 0.65 0.01 0.745 0.00 0.010 0.02 row2 0.13 0.000 0.395 0.500 0.25 0.45 0.000 0.34 0.440 0.79 row3 0.00 0.310 0.000 0.545 0.00 0.00 0.230 0.00 0.075 0.00 row4 0.00 0.100 0.395 0.760 0.24 0.00 0.070 0.42 0.300 0.46

pd.DataFrame.groupby

df.groupby(['row', 'col'])['val0', 'val1'].mean().unstack(fill_value=0)

Question 7

Can I subdivide by multiple columns?

pd.DataFrame.pivot_table
```
df.pivot_table(
values='val0', index='row', columns=['item', 'col'],
fill_value=0, aggfunc='mean')
```
item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 row row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.605 0.86 0.65 row2 0.35 0.00 0.37 0.00 0.00 0.44 0.00 0.00 0.13 0.000 0.50 0.13 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.000 0.28 0.00 row4 0.15 0.64 0.00 0.00 0.10 0.64 0.88 0.24 0.00 0.000 0.00 0.00
pd.DataFrame.groupby
```
df.groupby(
['row', 'item', 'col']
```
)['val0'].mean().unstack(['item', 'col']).fillna(0).sort_index(1)

Question 8

Can I subdivide by multiple columns?

pd.DataFrame.pivot_table
```
df.pivot_table(
values='val0', index=['key', 'row'], columns=['item', 'col'],
fill_value=0, aggfunc='mean')
```
item item0 item1 item2 col col2 col3 col4 col0 col1 col2 col3 col4 col0 col1 col3 col4 key row key0 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.86 0.00 row2 0.00 0.00 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 row3 0.00 0.00 0.00 0.00 0.31 0.00 0.81 0.00 0.00 0.00 0.00 0.00 row4 0.15 0.64 0.00 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 key1 row0 0.00 0.00 0.00 0.77 0.00 0.00 0.00 0.00 0.00 0.81 0.00 0.65 row2 0.35 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.13 row3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 row4 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 key2 row0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.00 0.00 row2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 row4 0.00 0.00 0.00 0.00 0.00 0.64 0.88 0.00 0.00 0.00 0.00 0.00
pd.DataFrame.groupby
```
df.groupby(
['key', 'row', 'item', 'col']
```
)['val0'].mean().unstack(['item', 'col']).fillna(0).sort_index(1)
pd.DataFrame.set_index because the set of keys are unique for both rows and columns
```
df.set_index(
['key', 'row', 'item', 'col']
```
)['val0'].unstack(['item', 'col']).fillna(0).sort_index(1)

Question 9

Can I aggregate the frequency in which the column and rows occur together, aka "cross tabulation"?

pd.DataFrame.pivot_table
```
df.pivot_table(index='row', columns='col', fill_value=0, aggfunc='size')
```
col col0 col1 col2 col3 col4 row row0 1 2 0 1 1 row2 1 0 2 1 2 row3 0 1 0 2 0 row4 0 1 2 2 1

pd.DataFrame.groupby

df.groupby(['row', 'col'])['val0'].size().unstack(fill_value=0)

pd.crosstab
```
pd.crosstab(df['row'], df['col'])
```
pd.factorize + np.bincount
```
# get integer factorization `i` and unique values `r`
```
for column 'row'

i, r = pd.factorize(df['row'].values)

get integer factorization j and unique values c

for column 'col'

j, c = pd.factorize(df['col'].values)

n will be the number of rows

m will be the number of columns

n, m = r.size, c.size

i * m + j is a clever way of counting the

factorization bins assuming a flat array of length

n * m. Which is why we subsequently reshape as (n, m)

b = np.bincount(i * m + j, minlength=n * m).reshape(n, m)

BTW, whenever I read this, I think 'Bean, Rice, and Cheese'

pd.DataFrame(b, r, c)
```
  col3  col2  col0  col1  col4
```
row3 2 0 0 1 0 row2 1 2 1 0 2 row0 1 0 1 2 1 row4 2 2 0 1 1
pd.get_dummies
```
pd.get_dummies(df['row']).T.dot(pd.get_dummies(df['col']))

  col0  col1  col2  col3  col4
```
row0 1 2 0 1 1 row2 1 0 2 1 2 row3 0 1 0 2 0 row4 0 1 2 2 1

Question 10

How do I convert a DataFrame from long to wide by pivoting on ONLY two columns?

DataFrame.pivot

The first step is to assign a number to each row - this number will be the row index of that value in the pivoted result. This is done using [GroupBy.cumcount](https://pandas.pydata.org/pandas- docs/stable/reference/api/pandas.core.groupby.GroupBy.cumcount.html):

    df2.insert(0, 'count', df2.groupby('A').cumcount())
df2

   count  A   B
0      0  a   0
1      1  a  11
2      2  a   2
3      3  a  11
4      0  b  10
5      1  b  10
6      2  b  14
7      0  c   7

The second step is to use the newly created column as the index to call [DataFrame.pivot](https://pandas.pydata.org/pandas- docs/stable/generated/pandas.DataFrame.pivot.html).

    df2.pivot(*df2)
# df2.pivot(index='count', columns='A', values='B')

A         a     b    c
count
0       0.0  10.0  7.0
1      11.0  10.0  NaN
2       2.0  14.0  NaN
3      11.0   NaN  NaN

DataFrame.pivot_table

Whereas [DataFrame.pivot](https://pandas.pydata.org/pandas- docs/stable/generated/pandas.DataFrame.pivot.html) only accepts columns, [DataFrame.pivot_table](https://pandas.pydata.org/pandas- docs/stable/reference/api/pandas.DataFrame.pivot_table.html) also accepts arrays, so the GroupBy.cumcount can be passed directly as the index without creating an explicit column.

    df2.pivot_table(index=df2.groupby('A').cumcount(), columns='A', values='B')

A         a     b    c
0       0.0  10.0  7.0
1      11.0  10.0  NaN
2       2.0  14.0  NaN
3      11.0   NaN  NaN

Question 11

How do I flatten the multiple index to single index after pivot

If columns type object with string join

df.columns = df.columns.map('|'.join)

else format

df.columns = df.columns.map('{0[0]}|{0[1]}'.format)

Question

Setup

Questions

Answer

Question 1

Examples

Questions 2 and 3

Question 4

Question 5

Question 6

Question 7

Question 8

Question 9

for column `'row'`

get integer factorization `j` and unique values `c`

for column `'col'`

`n` will be the number of rows

`m` will be the number of columns

`i * m + j` is a clever way of counting the

factorization bins assuming a flat array of length

`n * m`. Which is why we subsequently reshape as `(n, m)`

BTW, whenever I read this, I think 'Bean, Rice, and Cheese'

Question 10

Question 11

Hot

Latest

How can I pivot a dataframe?

Question

Setup

Questions

Answer

Question 1

Examples

Questions 2 and 3

Question 4

Question 5

Question 6

Question 7

Question 8

Question 9

for column 'row'

get integer factorization j and unique values c

for column 'col'

n will be the number of rows

m will be the number of columns

i * m + j is a clever way of counting the

factorization bins assuming a flat array of length

n * m. Which is why we subsequently reshape as (n, m)

BTW, whenever I read this, I think 'Bean, Rice, and Cheese'

Question 10

Question 11

Hot

Latest

for column `'row'`

get integer factorization `j` and unique values `c`

for column `'col'`

`n` will be the number of rows

`m` will be the number of columns

`i * m + j` is a clever way of counting the

`n * m`. Which is why we subsequently reshape as `(n, m)`