熊猫数据帧中如何将整数转换为浮点数？
Pandas Dataframe 提供了更改列值数据类型的自由。我们可以将它们从整数改为浮点类型，从整数改为字符串，从字符串改为整数，等等。
有两种方法可以将整数转换为浮点数:
方法 1: 使用数据框. astype() 方法
语法:
DataFrame.astype(dtype, copy=True, errors=’raise’, **kwargs)
示例 1: 使用数据框. a type()将一个列从 int 转换为 float
蟒蛇 3
# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176],
['A.B.D Villers', 38, 74, 3428000, 175],
['V.Kholi', 31, 70, 8428000, 172],
['S.Smith', 34, 80, 4428000, 180],
['C.Gayle', 40, 100, 4528000, 200],
['J.Root', 33, 72, 7028000, 170],
['K.Peterson', 42, 85, 2528000, 190]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
'Name', 'Age', 'Weight', 'Salary', 'Strike_rate'])
# lets find out the data type
# of 'Weight' column
print(df.dtypes)
输出:
让我们将重量类型转换为浮动
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# Now we will convert it from 'int' to 'float' type
# using DataFrame.astype() function
df['Weight'] = df['Weight'].astype(float)
print()
# lets find out the data type after changing
print(df.dtypes)
# print dataframe.
df
输出:
在上例中，我们将列“权重”的数据类型从“int64”更改为“float64”。
示例 2: 使用 DataFrame.astype() 将多个列从 int 转换为 float
蟒蛇 3
# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176],
['A.B.D Villers', 38, 74, 3428000, 175],
['V.Kholi', 31, 70, 8428000, 172],
['S.Smith', 34, 80, 4428000, 180],
['C.Gayle', 40, 100, 4528000, 200],
['J.Root', 33, 72, 7028000, 170],
['K.Peterson', 42, 85, 2528000, 190]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
'Name', 'Age', 'Weight', 'Salary', 'Strike_rate'])
# lets find out the data type of 'Age'
# and 'Strike_rate' columns
print(df.dtypes)
输出:
让我们将年龄和罢工率转换为浮动类型
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# now Pass a dictionary to astype() function
# which contains two columns
# and hence convert them from int to float type
df = df.astype({"Age":'float', "Strike_rate":'float'})
print()
# lets find out the data type after changing
print(df.dtypes)
# print dataframe.
df
输出:
在上例中，我们将列“ Age ”和“Strike_rate”的数据类型从“int64”更改为“float64”。
方法二:使用熊猫. to_numeric() 方法
语法:
pandas.to_numeric(arg, errors=’raise’, downcast=None)
示例 1: 使用 pandas.to_numeric() 将单个列从 int 转换为 float
蟒蛇 3
# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176], 4
['A.B.D Villers', 38, 74, 3428000, 175],
['V.Kholi', 31, 70, 8428000, 172],
['S.Smith', 34, 80, 4428000, 180],
['C.Gayle', 40, 100, 4528000, 200],
['J.Root', 33, 72, 7028000, 170],
['K.Peterson', 42, 85, 2528000, 190]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
'Name', 'Age', 'Weight', 'Salary', 'Height'])
# lets find out the data type of
# 'Weight' column
print(df.dtypes)
输出:
让我们把重量从整数转换成浮点数
蟒蛇 3
# Now we will convert it from 'int' to 'float' type
# using pandas.to_numeric()
df['Weight'] = pd.to_numeric(df['Weight'], downcast='float')
print()
# lets find out the data type after changing
print(df.dtypes)
# print dataframe.
df
输出:
在上例中，我们将列“权重”的数据类型从“int64”更改为“float32”。
例 2:
蟒蛇 3
# importing pandas library
import pandas as pd
# Initializing the nested list with Data set
player_list = [['M.S.Dhoni', 36, 75, 5428000, 176],
['A.B.D Villers', 38, 74, 3428000, 175],
['V.Kholi', 31, 70, 8428000, 172],
['S.Smith', 34, 80, 4428000, 180],
['C.Gayle', 40, 100, 4528000, 200],
['J.Root', 33, 72, 7028000, 170],
['K.Peterson', 42, 85, 2528000, 190]]
# creating a pandas dataframe
df = pd.DataFrame(player_list, columns=[
'Name', 'Experience', 'Weight', 'Salary', 'Height'])
# lets find out the data type of
# 'Experience' and 'Height' columns
print(df.dtypes)
输出:
让我们将经验和高度从 int 转换为 float
蟒蛇 3
# Now we will convert them from 'int' to 'float' type
# using pandas.to_numeric()
df['Experience'] = pd.to_numeric(df['Experience'], downcast='float')
df['Height'] = pd.to_numeric(df['Height'], downcast='float')
print()
# lets find out the data type after changing
print(df.dtypes)
# print dataframe.
df
输出:
在上面的示例中，我们将列“Experience”和“Height”的数据类型从“int64”更改为“float32”。
