php完整电商网站开发源码,建设银行官方网站打不开,建设网站说只给前端源码是什么意思,步骤1在本文中#xff0c;我们将看到如何在Pandas DataFrame中将True/False映射到1/0。True/False到1/0的转换在执行计算时至关重要#xff0c;并且可以轻松分析数据。
1. replace方法
在这个例子中#xff0c;我们使用Pandas replace()方法将True/False映射到1/0。在这里…在本文中我们将看到如何在Pandas DataFrame中将True/False映射到1/0。True/False到1/0的转换在执行计算时至关重要并且可以轻松分析数据。
1. replace方法
在这个例子中我们使用Pandas replace()方法将True/False映射到1/0。在这里我们创建了一个名为df的示例DataFrame其中有两个列‘Column 1’和’Column 2’包含布尔值。然后我们在DataFrame df上使用.replace()方法。我们提供了一个字典其中我们指定True应该被替换为1False应该被替换为0。
# Python code to map Boolean values to integer using .replace() method
import pandas as pd# Create a sample DataFrame with two columns, Column1
# and Column2, containing Boolean values
data {Column1: [True, False, True, False],Column2: [False, True, False, True]}# Create a DataFrame named df using the provided data
df pd.DataFrame(data)# Print the original DataFrame df containing Boolean values
print(df, \n)# Use the .replace() method to map True/False to 1/0
df df.replace({True: 1, False: 0})# Print the updated DataFrame df where Boolean values
# are now represented as integers (1/0)
print(df) 2. applymap函数
在这个例子中我们使用Pandas applymap()。在这里创建包含布尔值的示例DataFrame df。然后我们在DataFrame df上使用.applymap()函数。lambda函数lambda x1 if x else 0按元素应用于DataFrame中的每个值。它检查x值是否为True如果是则返回1否则返回0。
# Import the pandas library and alias it as pd
import pandas as pd# Create a sample DataFrame with two columns,
# Column1 and Column2, containing Boolean values
data {Column1: [True, False, True, False],Column2: [False, True, False, True]}# Create a DataFrame named df using the provided data
df pd.DataFrame(data)# Print the original DataFrame df containing Boolean values
print(df, \n)# Use .applymap() with a lambda function to map True/False to 1/0
df df.applymap(lambda x: 1 if x else 0)# Print the updated DataFrame df where Boolean
# values are now represented as integers (1/0)
print(df) 3. astype方法
# Import the pandas library and alias it as pd
import pandas as pd# Create a sample DataFrame with two columns, Column1
# and Column2, containing Boolean values
data {Column1: [True, False, True, False],Column2: [False, True, False, True]}# Create a DataFrame named df using the provided data
df pd.DataFrame(data)# Print the original DataFrame df containing Boolean values
print(df,/n)# Convert the Column1 and Column2 columns from
# Boolean (True/False) to integers (1/0)
df[Column1] df[Column1].astype(int)
df[Column2] df[Column2].astype(int)# Print the updated DataFrame df where Boolean
# values are now represented as integers
print(df) 4. apply和lambda函数
# Import the pandas library and alias it as pd
import pandas as pd# Create a sample DataFrame with two columns, Column1
# and Column2, containing Boolean values
data {Column1: [True, False, True, False],Column2: [False, True, False, True]}# Create a DataFrame named df using the provided data
df pd.DataFrame(data)
print(df,\n)# We define a lambda function that converts True to 1 and False
# to 0 and apply it to each column using .apply()
df_apply df.apply(lambda x: x.apply(lambda y: 1 if y else 0))# Print the DataFrame df_apply with the mapping applied
# using .apply() and a lambda function
print(\nUsing .apply() method with lambda function:)
print(df_apply) 5. map方法
# Import the pandas library and alias it as pd
import pandas as pd# Create a sample DataFrame with two columns, Column1 and
# Column2, containing Boolean values
data {Column1: [True, False, True, False],Column2: [False, True, False, True]}# Create a DataFrame named df using the provided data
df pd.DataFrame(data)
print(df,\n)# We use .map() on a specific column and provide a dictionary to perform the mapping
df[Column1] df[Column1].map({True: 1, False: 0})# Print the updated Column1 in the original DataFrame
# df where Boolean values are mapped to integers
print(\nUsing .map() method for Column1:)
print(df)
