对熊猫数据框中的每一行应用功能
原文:https://www.geeksforgeeks.org/apply-function-to-每行一个熊猫-dataframe/
Python 是执行数据分析任务的优秀语言。它提供了大量的类和函数，有助于以更简单的方式分析和操作数据。 可以使用 apply()函数将函数应用于给定数据框中的每一行。让我们看看做这项工作的方法。 例 1:
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# 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()
输出:
示例#2: 您也可以使用 numpy 函数作为数据帧的参数。
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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 column
df['add'] = df.apply(np.sum, axis = 1)
print('\nAfter Applying Function: ')
# printing the new dataframe
print(df)
if __name__ == '__main__':
main()
输出:
示例#3: 标准化数据
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# 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()
输出:
示例#4: 生成范围
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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 column
df = df.apply(lambda row : replace(row))
print('After Applying Function: ')
# printing the new dataframe
print(df)
if __name__ == '__main__':
main()
输出:
