使用 Excel 文件创建数据框

让我们看看如何使用Pandas将 excel 文件读取到 Pandas 数据框对象。
代码 #1：使用 pandas 的 read_excel() 方法读取一个 excel 文件。

Python3

# import pandas lib as pd
import pandas as pd
 
# read by default 1st sheet of an excel file
dataframe1 = pd.read_excel('SampleWork.xlsx')
 
print(dataframe1)

输出 ：

Name  Age    Stream  Percentage
0      Ankit   18      Math          95
1      Rahul   19   Science          90
2    Shaurya   20  Commerce          85
3  Aishwarya   18      Math          80
4   Priyanka   19   Science          75

代码 #2：使用 read_excel() 方法的 'sheet_name' 读取特定表格。

Python3

# import pandas lib as pd
import pandas as pd
 
# read 2nd sheet of an excel file
dataframe2 = pd.read_excel('SampleWork.xlsx', sheet_name = 1)
 
print(dataframe2)

输出 ：

Name  Age    Stream  Percentage
0     Priya   18      Math          95
1  shivangi   19   Science          90
2      Jeet   20  Commerce          85
3    Ananya   18      Math          80
4   Swapnil   19   Science          75

代码 #3：使用 read_excel() 方法的“usecols”参数读取特定列。

Python3

# import pandas lib as pd
import pandas as pd
 
require_cols = [0, 3]
 
# only read specific columns from an excel file
required_df = pd.read_excel('SampleWork.xlsx', usecols = require_cols)
 
print(required_df)

输出 ：

Name  Percentage
0      Ankit          95
1      Rahul          90
2    Shaurya          85
3  Aishwarya          80
4   Priyanka          75

代码 #4：使用 read_excel() 方法的 'na_values' 参数处理缺失数据。

Python3

# import pandas lib as pd
import pandas as pd
 
# Handling missing values of 3rd sheet of an excel file.
dataframe = pd.read_excel('SampleWork.xlsx', na_values = "Missing",
                                                    sheet_name = 2)
 
print(dataframe)

输出 ：

Name  Age   Stream  Percentage
0     Priya   18     Math          95
1  shivangi   19  Science          90
2      Jeet   20      NaN          85
3    Ananya   18     Math          80
4   Swapnil   19  Science          75

代码 #5：使用 read_excel() 方法的 'skiprows' 参数读取 Excel 文件时跳过起始行。

Python3

# import pandas lib as pd
import pandas as pd
 
# read 2nd sheet of an excel file after
# skipping starting two rows
df = pd.read_excel('SampleWork.xlsx', sheet_name = 1, skiprows = 2)
 
print(df)

输出 ：

shivangi  19   Science  90
0     Jeet  20  Commerce  85
1   Ananya  18      Math  80
2  Swapnil  19   Science  75

代码 #6 ：使用 read_excel() 方法的 'header' 参数将标题设置为任何行并从该行开始读取。

Python3

# import pandas lib as pd
import pandas as pd
 
# setting the 3rd row as header.
df = pd.read_excel('SampleWork.xlsx', sheet_name = 1, header = 2)
 
print(df)

输出 ：

shivangi  19   Science  90
0     Jeet  20  Commerce  85
1   Ananya  18      Math  80
2  Swapnil  19   Science  75

代码 #7：使用 read_excel() 方法的“sheet_name”参数读取多个 Excel 表格。

Python3

# import pandas lib as pd
import pandas as pd
 
# read both 1st and 2nd sheet.
df = pd.read_excel('SampleWork.xlsx', na_values = "Missing",
                                        sheet_name =[0, 1])
 
print(df)

输出 ：

OrderedDict([(0,         Name  Age    Stream  Percentage
0      Ankit   18      Math          95
1      Rahul   19   Science          90
2    Shaurya   20  Commerce          85
3  Aishwarya   18      Math          80
4   Priyanka   19   Science          75),

(1,        Name  Age    Stream  Percentage
0     Priya   18      Math          95
1  shivangi   19   Science          90
2      Jeet   20  Commerce          85
3    Ananya   18      Math          80
4   Swapnil   19   Science          75)])

代码 #8：使用 read_excel() 方法的“sheet_name”参数一起读取 excel 文件的所有表格。

Python3

# import pandas lib as pd
import pandas as pd
 
# read all sheets together.
all_sheets_df = pd.read_excel('SampleWork.xlsx', na_values = "Missing",
                                                     sheet_name = None)
 
print(all_sheets_df)

输出 ：

OrderedDict([('Sheet1',         Name  Age    Stream  Percentage
0      Ankit   18      Math          95
1      Rahul   19   Science          90
2    Shaurya   20  Commerce          85
3  Aishwarya   18      Math          80
4   Priyanka   19   Science          75),

('Sheet2',        Name  Age    Stream  Percentage
0     Priya   18      Math          95
1  shivangi   19   Science          90
2      Jeet   20  Commerce          85
3    Ananya   18      Math          80
4   Swapnil   19   Science          75), 

('Sheet3',        Name  Age   Stream  Percentage
0     Priya   18     Math          95
1  shivangi   19  Science          90
2      Jeet   20      NaN          85
3    Ananya   18     Math          80
4   Swapnil   19  Science          75)])