Apply function to every row in a Pandas DataFrame
Python is a great language for performing data analysis tasks. It provides with a huge amount of Classes and function which help in analyzing and manipulating data in an easier way.
One can use apply() function in order to apply function to every row in given dataframe. Let’s see the ways we can do this task.
Example #1:
# Import pandas package import pandas as pd # Function to add def add(a, b, c): return a + b + c def main(): # create a dictionary with # three fields each data = { 'A':[1, 2, 3], 'B':[4, 5, 6], 'C':[7, 8, 9] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) print("Original DataFrame:\n", df) df['add'] = df.apply(lambda row : add(row['A'], row['B'], row['C']), axis = 1) print('\nAfter Applying Function: ') # printing the new dataframe print(df) if __name__ == '__main__': main() |
Output:
Example #2:
You can use the numpy function as the parameters to the dataframe as well.
import pandas as pd import numpy as np def main(): # create a dictionary with # five fields each data = { 'A':[1, 2, 3], 'B':[4, 5, 6], 'C':[7, 8, 9] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) print("Original DataFrame:\n", df) # applying function to each row in the dataframe # and storing result in a new coloumn df['add'] = df.apply(np.sum, axis = 1) print('\nAfter Applying Function: ') # printing the new dataframe print(df) if __name__ == '__main__': main() |
Output:
Example #3: Normalising Data
# Import pandas package import pandas as pd def normalize(x, y): x_new = ((x - np.mean([x, y])) / (max(x, y) - min(x, y))) # print(x_new) return x_new def main(): # create a dictionary with three fields each data = { 'X':[1, 2, 3], 'Y':[45, 65, 89] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) print("Original DataFrame:\n", df) df['X'] = df.apply(lambda row : normalize(row['X'], row['Y']), axis = 1) print('\nNormalized:') print(df) if __name__ == '__main__': main() |
Output:
Example #4: Generate range
import pandas as pd import numpy as np pd.options.mode.chained_assignment = None # Function to generate range def generate_range(n): # printing the range for eg: # input is 67 output is 60-70 n = int(n) lower_limit = n//10 * 10 upper_limit = lower_limit + 10 return str(str(lower_limit) + '-' + str(upper_limit)) def replace(row): for i, item in enumerate(row): # updating the value of the row row[i] = generate_range(item) return row def main(): # create a dictionary with # three fields each data = { 'A':[0, 2, 3], 'B':[4, 15, 6], 'C':[47, 8, 19] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) print('Before applying function: ') print(df) # applying function to each row in # dataframe and storing result in a new coloumn df = df.apply(lambda row : replace(row)) print('After Applying Function: ') # printing the new dataframe print(df) if __name__ == '__main__': main() |
Output:
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