Python Pandas数据框架创建 [英] Python Pandas Data frame creation

问题描述

我尝试使用以下代码创建数据框df:

I tried to create a data frame df using the below code :

import numpy as np
import pandas as pd
index = [0,1,2,3,4,5]
s = pd.Series([1,2,3,4,5,6],index= index)
t = pd.Series([2,4,6,8,10,12],index= index)
df = pd.DataFrame(s,columns = ["MUL1"])
df["MUL2"] =t

print df


   MUL1  MUL2
0     1     2
1     2     4
2     3     6
3     4     8
4     5    10
5     6    12

在尝试使用以下语法创建相同的数据帧时，我得到了一个奇怪的输出.

While trying to create the same data frame using the below syntax, I am getting a wierd output.

df = pd.DataFrame([s,t],columns = ["MUL1","MUL2"])

print df

   MUL1  MUL2
0   NaN   NaN
1   NaN   NaN

请说明为什么当两个系列都不为空时在数据框中显示NaN的原因，为什么只显示两行而不显示其余的行.

Please explain why the NaN is being displayed in the dataframe when both the Series are non empty and why only two rows are getting displayed and no the rest.

通过使用pandas DataFrame方法中的column参数，还提供了创建与上述相同的数据框的正确方法.

Also provide the correct way to create the data frame same as has been mentioned above by using the columns argument in the pandas DataFrame method.

推荐答案

正确的方法之一是将包含这些序列的输入列表中的数组数据堆叠到列中-

One of the correct ways would be to stack the array data from the input list holding those series into columns -

In [161]: pd.DataFrame(np.c_[s,t],columns = ["MUL1","MUL2"])
Out[161]: 
   MUL1  MUL2
0     1     2
1     2     4
2     3     6
3     4     8
4     5    10
5     6    12

在后台，堆栈会创建一个2D数组，然后将其转换为数据框.这是堆叠数组的样子-

Behind the scenes, the stacking creates a 2D array, which is then converted to a dataframe. Here's what the stacked array looks like -

In [162]: np.c_[s,t]
Out[162]: 
array([[ 1,  2],
       [ 2,  4],
       [ 3,  6],
       [ 4,  8],
       [ 5, 10],
       [ 6, 12]])

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