["python","pandas","group-by","pivot","pivot-table"]
How can I pivot a dataframe?

How do I pivot the pandas dataframe below such that the col values become columns, row values become the index, and mean of val0 becomes the values? (In some cases this is called transforming from long-format to wide-format.) Consider a dataframe df with columns 'key', 'row', 'item', 'col', and random float values 'val0', 'val1'. I conspicuously named the columns and relevant column values to correspond with how I want to pivot them. (Setup code at bottom.) 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 Subquestions How to avoid getting ValueError: Index contains duplicate entries, cannot reshape? How do I pivot df such that the col values become columns, row values become 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 pivot... ... 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 ... to do an aggregate function other than mean, like 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 ... to 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 ... to 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 ... to subdivide by multiple columns? (item0,item1,item2..., col0,col1,col2...) 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 ... to subdivide by multiple rows: (key0,key1... row0,row1,row2...) 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 ... to 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 ... to 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 multi-index to single index after pivot? From: 1 2 1 1 2 a 2 1 1 b 2 1 0 c 1 0 0 To: 1|1 2|1 2|2 a 2 1 1 b 2 1 0 c 1 0 0 Setup 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) Why is this question not a duplicate? and more useful than the following autosuggestions: How to pivot a dataframe in Pandas? only covers the specific case of 'Country' to row-index, values of 'Indicator' for 'Year' to multiple columns and no aggregation of values. When pivoting a Pandas dataframe, how do I make the column names the same as in R? (flat, label in column names) asks how to pivot in pandas like in R, i.e. autogenerate an individual column for each value of strength... pandas pivoting a dataframe, duplicate rows asks about the syntax for pivoting multiple columns, without needing to list them all. None of the existing questions and answers are comprehensive, so this is an attempt at a canonical question and answer that encompasses all aspects of pivoting.
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. 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)