python – How can I pivot a dataframe?

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 is 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.

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 meanlike 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 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.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)

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