从 Numpy 数组创建 Pandas DataFrame，并指定索引列和列标题
本文演示了多个示例来将 Numpy 数组转换为 Pandas Dataframe 并指定数据帧的索引列和列标题。
示例 1: 在本例中，将生成 Pandas 数据框，并在函数中提到索引列和列标题的专有名称。当没有命名索引列或列标题的模式时，可以使用这种方法。
下面是实现:
蟒蛇 3
# 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: 在本例中，索引列和列标题通过迭代生成。行和列的迭代范围由 Numpy 数组的形状定义。每次迭代时，都会向预定义的字符串中添加一个数字，并生成新的索引列或列标题。因此，如果在命名数据帧的标签时有某种模式，这种方法是合适的。
下面是实现:
蟒蛇 3
# 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: 在本例中，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。标签名再次通过迭代生成，但是方法没有什么不同。这里，迭代次数由 Numpy 数组内部子数组的长度定义。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:
蟒蛇 3
# 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 数据帧的过程中，必须对 Numpy 数组的子数组进行适当的索引，以获得正确的数据帧标签序列。
下面是实现:
蟒蛇 3
# 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:
