If periods=2, then the percentage change will be computed using the values of two rows back. By default, periods=1, which means that the value in the previous row will be used to compute the percentage change.

2. fill_methodlink | string | optional

The rule by which to fill missing values:

Value	Description
`"backfill"` or `"bfill"`	Use the next non-`NaN` value to fill.
`"pad"` or `"ffill"`	Use the previous non-`NaN` value to fill.

By default, fill_method="pad".

WARNING

Regardless of fill_method, the first row will always have NaN since there is no prior value to compute the percentage change.

3. limit | int | optional

The number of consecutive NaN to fill before stopping to fill. By default, limit=None.

4. freqlink | string or timedelta or DateOffset | optional

The time interval to use for when DataFrame is a time-series. By default, freq=None.

Return Value

A DataFrame holding the percentage changes of the values in each column.

Examples

Basic usage

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[2,4,12], "B":[1,3,15]})
df
                
            
               A   B
0  2   1
1  4   3
2  12  15

To compute the percentage change of consecutive values for each column in df:


        
        
            
                
                
                    df.pct_change()
                
            
               A    B
0  NaN  NaN
1  1.0  2.0
2  2.0  4.0

Here, note the following:

the first row is always NaN because there is no prior value with which to compute the percentage change.
to explain how these percent changes are calculated, take for example the bottom-right value (4.0). This value is computed by taking the difference between the prior value in df (15-3=12), and then dividing this difference by the prior value (12/3=4.0).

Specifying periods

Consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[2,4,12], "B":[1,3,15]})
df
                
            
               A   B
0  2   1
1  4   3
2  12  15

By default, periods=1, which means that the previous row is used to compute the percentage change:


        
        
            
                
                
                    df.pct_change()   # periods=1
                
            
               A    B
0  NaN  NaN
1  1.0  2.0
2  2.0  4.0

To compute the percentage change with values in 2 rows back:


        
        
            
                
                
                    df.pct_change(periods=2)
                
            
               A    B
0  NaN  NaN
1  NaN  NaN
2  5.0  14.0

We get NaN for the second row because there is no row to compare with.

NOTE

To use the subsequent row to compute the percentage change, set periods=-1.

Specifying fill_method

Consider the following DataFrame with some missing values:


        
        
            
                
                
                    df = pd.DataFrame({"A":[2,pd.np.nan,12], "B":[1,3,pd.np.nan]})
df
                
            
               A     B
0  2.0   1.0
1  NaN   3.0
2  12.0  NaN

pad

By default, fill_method="pad", which means that the previous non-NaN value is used to fill NaN:


        
        
            
                
                
                    df.pct_change()   # fill_method="pad"
                
            
               A    B
0  NaN  NaN
1  0.0  2.0
2  5.0  0.0

Note that this is equivalent to calling pct_change() on the following:


        
        
            
                
                
                    pd.DataFrame({"A":[2,2,12], "B":[1,3,3]})
                
            
               A   B
0  2   1
1  2   3
2  12  3

WARNING

Regardless of fill_method, the first row will always have NaN since there is no prior value with which to compute the percentage change.

bfill

To fill NaN using the next non-NaN value in the DataFrame:


        
        
            
                
                
                    df.pct_change(fill_method="bfill")
                
            
               A    B
0  NaN  NaN
1  5.0  2.0
2  0.0  NaN

Note that this is equivalent to calling pct_change() on the following:


        
        
            
                
                
                    pd.DataFrame({"A":[2,12,12], "B":[1,3,pd.np.NaN]})
                
            
               A   B
0  2   1
1  12  3
2  12  NaN

Notice how the NaN in the bottom-right corner is still a NaN - this is because there exists no non-NaN in the next row (there is no next row).

Specifying freq

Consider the following time-series DataFrame:


        
        
            
                
                
                    idx = pd.date_range(start="2020-12-20", periods=4)
df = pd.DataFrame({"A":[2,4,12,24], "B":[1,3,15,30]}, index=idx)
df
                
            
                        A  B
2020-12-20  2  1
2020-12-21  4  3
2020-12-22  12  15
2020-12-23  24  30

To compute the percentage change of every 2 days (e.g. 12-20 and 12-22):


        
        
            
                
                
                    df.pct_change(freq="2D")
                
            
                        A    B
2020-12-20  NaN  NaN
2020-12-21  NaN  NaN
2020-12-22  5.0  14.0
2020-12-23  5.0  9.0

Here, we get NaN values for the first 2 rows because there are no dates 12-18 and 12-19 with which to compute the percentage change.

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.pct_change.html

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