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.
Dataframe.applymap()
method applies a function that accepts and returns a scalar to every element of a DataFrame.
Syntax: DataFrame.applymap(func) Parameters: func: Python function, returns a single value from a single value. Returns: Transformed DataFrame.
For link to CSV file Used in Code, click here
Example #1: Apply the applymap()
function on the dataframe to find the no. of characters in all cells.
# importing pandas as pd import pandas as pd # Making data frame from the csv file df = pd.read_csv( "nba.csv" ) # Printing the first 10 rows of # the data frame for visualization df[: 10 ] |
# Using lambda function we first convert all # the cell to a string value and then find # its length using len() function df.applymap( lambda x: len ( str (x))) |
Output:
Notice how all nan value has been converted to string nan and their length is evaluated to be 3.
Example #2: Append _X
in each cell using applymap()
function.
In order to append _X
in each cell, first convert each cell into a string.
# importing pandas as pd import pandas as pd # Making data frame from the csv file df = pd.read_csv( "nba.csv" ) # Using applymap() to append '_X' # in each cell of the dataframe df.applymap( lambda x: str (x) + '_X' ) |
Output:
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