Here is a list of idioms we can use to pivot

1. 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.

2. 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.

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 its 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.

See also:

• Reshaping and pivot tables — pandas User Guide

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. 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:

  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.

  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 only accepts columns, DataFrame.pivot_table 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)

To extend @piRSquared’s answer another version of Question 10

Question 10.1

DataFrame:

d = data = {'A': {0: 1, 1: 1, 2: 1, 3: 2, 4: 2, 5: 3, 6: 5},
 'B': {0: 'a', 1: 'b', 2: 'c', 3: 'a', 4: 'b', 5: 'a', 6: 'c'}}
df = pd.DataFrame(d)

   A  B
0  1  a
1  1  b
2  1  c
3  2  a
4  2  b
5  3  a
6  5  c

Output:

   0     1     2
A
1  a     b     c
2  a     b  None
3  a  None  None
5  c  None  None

Using df.groupby and pd.Series.tolist

t = df.groupby('A')['B'].apply(list)
out = pd.DataFrame(t.tolist(),index=t.index)
out
   0     1     2
A
1  a     b     c
2  a     b  None
3  a  None  None
5  c  None  None

Or
A much better alternative using pd.pivot_table with df.squeeze.

t = df.pivot_table(index='A',values='B',aggfunc=list).squeeze()
out = pd.DataFrame(t.tolist(),index=t.index)

To better understand how the function pivot works you can look at the example from Pandas documentation. However pivot will fail if you have repeating index-columns (foo–bar) combinations (like df in the second example):

In opposite to pivot the function pivot_table supports data aggregation using the mean function by default. Here is an example with the sum aggregation function:

Call reset_index() (along with add_suffix())

Oftentimes, reset_index() is needed after you call pivot_table or pivot. For example, to make the following transformation (where one column become column names)

you use the following code, where after pivot, you add prefix to the newly created column names and convert the index (in this case "movies") back into a column and remove the name of the axis name:

df.pivot(index='movie', columns='week', values='sales').add_prefix('week_').reset_index().rename_axis(columns=None)

As the other answers mentioned, “pivot” may refer to 2 different operations:

1. Unstacked aggregation (i.e. make the results of groupby.agg wider.)
2. Reshaping (similar to pivot in Excel, reshape in numpy or pivot_wider in R)

1. Aggregation

pivot_table or crosstab are simply unstacked results of groupby.agg operation. In fact, the source code shows that, under the hood, the following are true:

• pivot_table = groupby + unstack (read here for more info.)
• crosstab = pivot_table

N.B. You can use list of column names as index, columns and values arguments.

df.groupby(rows+cols)[vals].agg(aggfuncs).unstack(cols)
# equivalently,
df.pivot_table(vals, rows, cols, aggfuncs)

1.1. crosstab is a special case of pivot_table; thus of groupby + unstack

The following are equivalent:

• pd.crosstab(df['colA'], df['colB'])
• df.pivot_table(index='colA', columns='colB', aggfunc='size', fill_value=0)
• df.groupby(['colA', 'colB']).size().unstack(fill_value=0)

Note that pd.crosstab has a significantly larger overhead, so it’s significantly slower than both pivot_table and groupby + unstack. In fact, as noted here, pivot_table is slower than groupby + unstack as well.

2. Reshaping

pivot is a more limited version of pivot_table where its purpose is to reshape a long dataframe into a long one.

df.set_index(rows+cols)[vals].unstack(cols)
# equivalently, 
df.pivot(index=rows, columns=cols, values=vals)

2.1. Augment rows/columns as in Question 10

You can also apply the insight from Question 10 to multi-column pivot operation as well. There are two cases:

• “long-to-long”: reshape by augmenting the indices

  

  Code:

  df = pd.DataFrame({'A': [1, 1, 1, 2, 2, 2], 'B': [*'xxyyzz'], 
                     'C': [*'CCDCDD'], 'E': [100, 200, 300, 400, 500, 600]})
  rows, cols, vals = ['A', 'B'], ['C'], 'E'
  
  # using pivot syntax
  df1 = (
      df.assign(ix=df.groupby(rows+cols).cumcount())
      .pivot(index=[*rows, 'ix'], columns=cols, values=vals)
      .fillna(0, downcast='infer')
      .droplevel(-1).reset_index().rename_axis(columns=None)
  )
  
  # equivalently, using set_index + unstack syntax
  df1 = (
      df
      .set_index([*rows, df.groupby(rows+cols).cumcount(), *cols])[vals]
      .unstack(fill_value=0)
      .droplevel(-1).reset_index().rename_axis(columns=None)
  )
  

• “long-to-wide”: reshape by augmenting the columns

  

  Code:

  df1 = (
      df.assign(ix=df.groupby(rows+cols).cumcount())
      .pivot(index=rows, columns=[*cols, 'ix'])[vals]
      .fillna(0, downcast='infer')
  )
  df1 = df1.set_axis([f"{c[0]}_{c[1]}" for c in df1], axis=1).reset_index()
  
  # equivalently, using the set_index + unstack syntax
  df1 = (
      df
      .set_index([*rows, df.groupby(rows+cols).cumcount(), *cols])[vals]
      .unstack([-1, *range(-2, -len(cols)-2, -1)], fill_value=0)
  )
  df1 = df1.set_axis([f"{c[0]}_{c[1]}" for c in df1], axis=1).reset_index()
  

• minimum case using the set_index + unstack syntax:

  

  Code:

  df1 = df.set_index(['A', df.groupby('A').cumcount()])['E'].unstack(fill_value=0).add_prefix('Col').reset_index()
  

¹ pivot_table() aggregates the values and unstacks it. Specifically, it creates a single flat list out of index and columns, calls groupby() with this list as the grouper and aggregates using the passed aggregator methods (the default is mean). Then after aggregation, it calls unstack() by the list of columns. So internally, pivot_table = groupby + unstack. Moreover, if fill_value is passed, fillna() is called.

In other words, the method that produces pv_1 is the same as the method that produces gb_1 in the example below.

pv_1 = df.pivot_table(index=rows, columns=cols, values=vals, aggfunc=aggfuncs, fill_value=0)
# internal operation of `pivot_table()`
gb_1 = df.groupby(rows+cols)[vals].agg(aggfuncs).unstack(cols).fillna(0, downcast="infer")
pv_1.equals(gb_1) # True

² crosstab() calls pivot_table(), i.e., crosstab = pivot_table. Specifically, it builds a DataFrame out of the passed arrays of values, filters it by the common indices and calls pivot_table(). It’s more limited than pivot_table() because it only allows a one-dimensional array-like as values, unlike pivot_table() that can have multiple columns as values.

The pivot function in pandas has the same functionality as the pivot operation in excel. We can transform a dataset from a long format to a wide format.

Lets have a example

We want to convert the dataset into a form such that each country becomes a column and the new confirmed cases as values corresponding to the countries. We can perform this data manipulation using the pivot function.

Pivot the dataset

pivot_df = pd.pivot(df, index =['Date'], columns ='Country', values =['NewConfirmed'])
## renaming the columns  
pivot_df.columns = df['Country'].sort_values().unique()

We can bring the new columns to the same level as the index column Data by resetting the index.

reset the index to modify the column levels

pivot_df = pivot_df.reset_index()