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.
Apply function to every row in a Pandas DataFrame
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:
Python3
# Import pandas packageimport pandas as pd# Function to adddef add(a, b, c): return a + b + cdef 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:
Original DataFrame:
A B C
0 1 4 7
1 2 5 8
2 3 6 9
After Applying Function:
A B C add
0 1 4 7 12
1 2 5 8 15
2 3 6 9 18
Example 2: You can use the numpy function as the parameters to the dataframe as well.
Python3
import pandas as pdimport 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 column df['add'] = df.apply(np.sum, axis = 1) print('\nAfter Applying Function: ') # printing the new dataframe print(df) if __name__ == '__main__': main() |
Output:
Original DataFrame:
A B C
0 1 4 7
1 2 5 8
2 3 6 9
After Applying Function:
A B C add
0 1 4 7 12
1 2 5 8 15
2 3 6 9 18
Example 3: Normalising Data
Python3
# Import pandas packageimport pandas as pddef normalize(x, y): x_new = ((x - np.mean([x, y])) / (max(x, y) - min(x, y))) # print(x_new) return x_newdef 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:
Original DataFrame:
X Y
0 1 45
1 2 65
2 3 89
Normalized:
X Y
0 -0.5 45
1 -0.5 65
2 -0.5 89
Example 4: Generate range
Python3
import pandas as pdimport numpy as np pd.options.mode.chained_assignment = None# Function to generate rangedef 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 column df = df.apply(lambda row : replace(row)) print('After Applying Function: ') # printing the new dataframe print(df) if __name__ == '__main__': main() |
Output:
Before applying function:
A B C
0 0 4 47
1 2 15 8
2 3 6 19
After Applying Function:
A B C
0 0-10 0-10 40-50
1 0-10 10-20 0-10
2 0-10 0-10 10-20
