对熊猫数据框中的单个或选定的列或行应用功能
原文:https://www.geesforgeks.org/apply-a-function-to-single-or-selected-columns-in-pandas-data frame/
在本文中，我们将学习对 Dataframe 中的单个或选定列或行应用函数的不同方法。我们将使用data frame/series.apply()方法来应用函数。
语法: Dataframe/series.apply(func，convert_dtype=True，args=()
参数:该方法将取以下参数: func: 取一个函数，应用于熊猫系列的所有值。 convert_dtype: 根据函数的操作转换 dtype。 args=(): 要传递给函数而不是序列的附加参数。
应用功能/操作后返回类型:熊猫系列。
方法 1: 使用Dataframe.apply()和lambda function。
例 1: 为柱
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
# Apply function numpy.square() to lambda
# to find the squares of the values of
# column whose column name is 'z'
new_df = df.apply(lambda x: np.square(x) if x.name == 'z' else x)
# Output
new_df
输出:
例 2: 为行。
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
# Apply function numpy.square() to lambda
# to find the squares of the values of row
# whose row index is 'b'
new_df = df.apply(lambda x: np.square(x) if x.name == 'b' else x,
axis = 1)
# Output
new_df
输出:
方法二:使用Dataframe/series.apply() &【运算符】。
例 1: 为柱。
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
# Apply a function to one column 'z'
# and assign it back to the same column
df['z'] = df['z'].apply(np.square)
# Output
df
输出:
例 2: 为行。
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
# Apply a function to one row 'b'
# and assign it back to the same row
df.loc['b'] = df.loc['b'].apply(np.square)
# Output
df
输出:
方法三:使用numpy.square()方法和[ ]算子。
例 1: 为柱
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
# Apply a function to one column 'z' and
# assign it back to the same column
df['z'] = np.square(df['z'])
# Output
print(df)
输出:
例 2: 为行。
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'), index = list('abc'))
# Apply a function to one row 'b' and
# assign it back to the same row
df.loc['b'] = np.square(df.loc['b'])
# Output
df
产量:
我们还可以将函数应用于数据框中的多列或多行。
实施例 1: 对于柱
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
# Apply function numpy.square()
# for square the values of
# two columns 'x' and 'y'
new_df = df.apply(lambda x: np.square(x) if x.name in ['x', 'y'] else x)
# Output
new_df
输出:
例 2: 为行。
# import pandas and numpy library
import pandas as pd
import numpy as np
# List of Tuples
matrix = [(1, 2, 3),
(4, 5, 6),
(7, 8, 9)
]
# Create a DataFrame object
df = pd.DataFrame(matrix, columns = list('xyz'),
index = list('abc'))
# Apply function numpy.square() to
# square the values of two rows
# 'b' and 'c'
new_df = df.apply(lambda x: np.square(x) if x.name in ['b', 'c'] else x,
axis = 1)
# Output
new_df
输出:
