Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.diff() is used to find the first discrete difference of objects over the given axis. We can provide a period value to shift for forming the difference.
Syntax: DataFrame.diff(periods=1, axis=0)
Parameters:
periods : Periods to shift for forming difference
axis : Take difference over rows (0) or columns (1).Returns: diffed : DataFrame
Example #1: Use diff() function to find the discrete difference over the index axis with period value equal to 1.
# importing pandas as pdimport pandas as pd # Creating the dataframedf = pd.DataFrame({"A":[5, 3, 6, 4], "B":[11, 2, 4, 3], "C":[4, 3, 8, 5], "D":[5, 4, 2, 8]}) # Print the dataframedf |
Now find the discrete difference over the index axis.
# To find the discrete differencedf.diff(axis = 0, periods = 1) |
Output :
The output is a dataframe with cells containing the discrete difference over the index axis. The value present in each cell is the difference of current cell value with the previous row corresponding cell. Notice, the first row is NaN filled. This is because there is no row above that to find the difference with so it is treated as NaN.
Example #2: Use diff() function to find the discrete difference over the column axis with period value equal to 1.
# importing pandas as pdimport pandas as pd # Creating the dataframedf = pd.DataFrame({"A":[5, 3, 6, 4], "B":[11, 2, 4, 3], "C":[4, 3, 8, 5], "D":[5, 4, 2, 8]}) # To find the discrete differencedf.diff(axis = 1, periods = 1) |
Output :
The output is a dataframe with cells containing the discrete difference over the column axis. The value present in each cell is the difference of current cell value with the previous column corresponding cell. Notice, the first column is NaN filled. This is because there is no column to the left of it to find the difference with so it is treated as NaN.
