从 Numpy 数组创建 Pandas DataFrame 并指定索引列和列标题

本文演示了将 Numpy 数组转换为 Pandas Dataframe 并为数据框指定索引列和列标题的多个示例。

示例 1：在此示例中，将生成 Pandas 数据框，并在函数中提及索引列和列标题的专有名称。当在命名索引列或列标题时没有模式时，可以使用此方法。

下面是实现：

Python3

# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index 
# column and column headers
  
# import required libraries
import numpy as np
import pandas as pd
  
# creating a numpy array
numpyArray = np.array([[15, 22, 43], 
                       [33, 24, 56]])
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# name of index and columns
panda_df = pd.DataFrame(data = numpyArray, 
                        index = ["Row_1", "Row_2"], 
                        columns = ["Column_1",
                                   "Column_2", "Column_3"])
  
# printing the dataframe
print(panda_df)

输出：

示例2：本示例中，索引列和列标题是通过迭代生成的。行和列的迭代范围由 Numpy 数组的形状定义。每次迭代都会将一个数字添加到预定义的字符串中，并生成新的索引列或列标题。因此，如果在命名数据框的标签时存在某种模式，则这种方法是合适的。

下面是实现：

Python3

# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index column 
# and column headers
  
# import required libraries
import pandas as pd
import numpy as np
  
# creating a numpy array
numpyArray = np.array([[15, 22, 43], 
                       [33, 24, 56]])
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# name of index and columns
panda_df = pd.DataFrame(data = numpyArray[0:, 0:],
                        index = ['Row_' + str(i + 1) 
                        for i in range(numpyArray.shape[0])],
                        columns = ['Column_' + str(i + 1) 
                        for i in range(numpyArray.shape[1])])
  
# printing the dataframe
print(panda_df)

输出：

示例 3：在此示例中，索引列和列标题是在将 Numpy 数组转换为 Pandas 数据帧之前定义的。标签名称再次通过迭代生成，但方法略有不同。这里，迭代次数由 Numpy 数组中子数组的长度定义。如果索引列和列标题名称遵循某种模式，则可以使用此方法。

下面是实现：

Python3

# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index column 
# and column headers
  
# import required libraries
import pandas as pd
import numpy as np
  
# creating a numpy array
numpyArray = np.array([[15, 22, 43], 
                       [33, 24, 56]])
  
# defining index for the 
# Pandas dataframe
index = ['Row_' + str(i) 
        for i in range(1, len(numpyArray) + 1)]
  
# defining column headers for the 
# Pandas dataframe
columns = ['Column_' + str(i) 
          for i in range(1, len(numpyArray[0]) + 1)]
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# details of index and column headers
panda_df = pd.DataFrame(numpyArray , 
                        index = index,
                        columns = columns)
  
# printing the dataframe
print(panda_df)

输出：

示例 #4：在这种方法中，Pandas 数据帧的索引列和列标题将出现在 Numpy 数组中。在将 Numpy 数组转换为 Pandas 数据帧期间，必须对 Numpy 数组的子数组进行适当的索引，以便获得正确的数据帧标签序列。

下面是实现：

Python3

# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index column 
# and column headers
  
# import required libraries
import pandas as pd
import numpy as np
  
# creating a numpy array and
# specifying the index and 
# column headers along with 
# data stored in the array
numpyArray = np.array([['', 'Column_1', 
                       'Column_2', 'Column_3'],
                       ['Row_1', 15, 22, 43],
                       ['Row_2', 33, 24, 56]])
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# details of index and column headers
panda_df = pd.DataFrame(data = numpyArray[1:, 1:],
                        index = numpyArray[1:, 0],
                        columns = numpyArray[0, 1:])
  
# printing the dataframe
print(panda_df)

输出：