使用列表创建 Pandas 数据框
Pandas DataFrame 是一种二维标记数据结构,具有可能不同类型的列。它通常是最常用的 pandas 对象。
Pandas DataFrame 可以通过多种方式创建。让我们讨论如何使用列表创建 Pandas 数据框。
代码#1:
Python3
# Import pandas library
import pandas as pd
# initialize list of lists
data = [['Geeks', 10], ['for', 15], ['geeks', 20]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Age'])
# print dataframe.
print(df )
Python3
# Import pandas library
import pandas as pd
# initialize list of lists
data = [['DS', 'Linked_list', 10], ['DS', 'Stack', 9], ['DS', 'Queue', 7],
['Algo', 'Greedy', 8], ['Algo', 'DP', 6], ['Algo', 'BackTrack', 5], ]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Category', 'Name', 'Marks'])
# print dataframe.
print(df )
Python3
# Import pandas library
import pandas as pd
# initialize list of lists
data = [[1, 5, 10], [2, 6, 9], [3, 7, 8]]
# Create the pandas DataFrame
df = pd.DataFrame(data)
# specifying column names
df.columns = ['Col_1', 'Col_2', 'Col_3']
# print dataframe.
print(df, "\n")
# transpose of dataframe
df = df.transpose()
print("Transpose of above dataframe is-\n", df)
输出:
Name Age
0 Geeks 10
1 for 15
2 geeks 20
代码#2:
Python3
# Import pandas library
import pandas as pd
# initialize list of lists
data = [['DS', 'Linked_list', 10], ['DS', 'Stack', 9], ['DS', 'Queue', 7],
['Algo', 'Greedy', 8], ['Algo', 'DP', 6], ['Algo', 'BackTrack', 5], ]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Category', 'Name', 'Marks'])
# print dataframe.
print(df )
输出:
Category Name Marks
0 DS Linked_list 10
1 DS Stack 9
2 DS Queue 7
3 Algo Greedy 8
4 Algo DP 6
5 Algo BackTrack 5
代码#3:对数据框进行一些操作。
Python3
# Import pandas library
import pandas as pd
# initialize list of lists
data = [[1, 5, 10], [2, 6, 9], [3, 7, 8]]
# Create the pandas DataFrame
df = pd.DataFrame(data)
# specifying column names
df.columns = ['Col_1', 'Col_2', 'Col_3']
# print dataframe.
print(df, "\n")
# transpose of dataframe
df = df.transpose()
print("Transpose of above dataframe is-\n", df)
输出:
Col_1 Col_2 Col_3
0 1 5 10
1 2 6 9
2 3 7 8
Transpose of above dataframe is-
0 1 2
Col_1 1 2 3
Col_2 5 6 7
Col_3 10 9 8