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 pdimport pandas as pd # Making data frame from the csv filedf = pd.read_csv("nba.csv") # Printing the first 10 rows of # the data frame for visualizationdf[:10] |
# Using lambda function we first convert all # the cell to a string value and then find# its length using len() functiondf.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 pdimport pandas as pd # Making data frame from the csv filedf = pd.read_csv("nba.csv") # Using applymap() to append '_X'# in each cell of the dataframedf.applymap(lambda x: str(x) + '_X') |
Output:
