• What is pivot?


• How do I pivot?


• Is this a pivot?


• Long format to wide format?

I've seen a lot of questions that ask about pivot tables. Even if they don't know that they are asking about pivot tables, they usually are. It is virtually impossible to write a canonical question and answer 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 a few examples from my google search

1. 
   How to pivot a dataframe in Pandas?
   
   
   • Good question and answer. But the answer only answers the specific question with little explanation.
   
   


2. 
   pandas pivot table to data frame
   
   
   • In this question, the 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.
   
   


3. 
   pandas pivoting a dataframe, duplicate rows
   
   
   • Another decent question but the answer focuses on one method, namely pd.DataFrame.pivot
     
   
   

So whenever someone searches for pivot they get sporadic results that are likely not going to answer their specific question.

Setup

You may notice that I conspicuously named my columns and relevant column values to correspond with how I'm going to pivot in the answers below. Pay attention so that you get familiar with where which column names go where to get the results you're looking for.

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

Question(s)

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



2. 
   

   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
   

   
   


3. 
   

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

   
   


4. 
   

   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
   

   
   


5. 
   

   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
   

   
   


6. 
   

   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
   

   
   


7. 
   

   Can 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
   

   
   


8. 
   

   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
   

   
   


9. 
   

   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
   

   
   

We start by answering the first question:

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 in which it is being asked to pivot on. 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()

Here is a list of idioms we can use to pivot

1. 
   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.
   
   


2. 
   pd.DataFrame.pivot_table
   
   
   • A glorified version of groupby with more intuitive API. For many people, this is the preferred approach. And is the intended approach by the developers.
   
   
   • Specify row level, column levels, values to be aggregated, and function(s) to perform aggregations.
   
   


3. 
   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.
   
   


4. 
   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.
   
   


5. 
   pd.crosstab
   
   
   • This a specialized version of pivot_table and in it's purest form is the most intuitive way to perform several tasks.
   
   


6. 
   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.
   
   


7. 
   pd.get_dummies + pd.DataFrame.dot
   
   
   • I use this for cleverly performing cross tabulation.
   
   

Examples

What I'm going to do for each subsequent answer and question is to answer it using pd.DataFrame.pivot_table. Then I'll provide alternatives to perform the same task.

Question 3

How do I pivot df such that the col values are columns, row values are the index, mean of val0 are the values, and 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. Notice I skipped question 2 as it's the same as this answer without the fill_value
    
  
  
  • 
    

    aggfunc='mean' is the default and I didn't have to set it. I included it to be explicit.

    
    
    

    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 cross_tab 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 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 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.cross_tab

  
  
  

  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