将列表读入 Pandas DataFrame 的列中
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Read lists into columns of pandas DataFrame
提问by Steven C. Howell
I want to load lists into columns of a pandas DataFrame but cannot seem to do this simply. This is an example of what I want using transpose()but I would think that is unnecessary:
我想将列表加载到 Pandas DataFrame 的列中，但似乎不能简单地做到这一点。这是我想要使用的示例，transpose()但我认为这是不必要的：
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: x = np.linspace(0,np.pi,10)
In [4]: y = np.sin(x)
In [5]: data = pd.DataFrame(data=[x,y]).transpose()
In [6]: data.columns = ['x', 'sin(x)']
In [7]: data
Out[7]:
x sin(x)
0 0.000000 0.000000e+00
1 0.349066 3.420201e-01
2 0.698132 6.427876e-01
3 1.047198 8.660254e-01
4 1.396263 9.848078e-01
5 1.745329 9.848078e-01
6 2.094395 8.660254e-01
7 2.443461 6.427876e-01
8 2.792527 3.420201e-01
9 3.141593 1.224647e-16
[10 rows x 2 columns]
Is there a way to directly load each list into a column to eliminate the transpose and insert the column labels when creating the DataFrame?
有没有办法在创建DataFrame时直接将每个列表加载到列中以消除转置并插入列标签？
回答by Steven C. Howell
Someone just recommended creating a dictionary from the data then loading that into the DataFrame like this:
有人只是建议从数据创建一个字典，然后像这样将其加载到 DataFrame 中：
In [8]: data = pd.DataFrame({'x': x, 'sin(x)': y})
In [9]: data
Out[9]:
x sin(x)
0 0.000000 0.000000e+00
1 0.349066 3.420201e-01
2 0.698132 6.427876e-01
3 1.047198 8.660254e-01
4 1.396263 9.848078e-01
5 1.745329 9.848078e-01
6 2.094395 8.660254e-01
7 2.443461 6.427876e-01
8 2.792527 3.420201e-01
9 3.141593 1.224647e-16
[10 rows x 2 columns]
Note than a dictionary is an unordered set of key-value pairs. If you care about the column orders, you should pass a list of the ordered key values to be used (you can also use this list to only include some of the dict entries):
请注意，字典是一组无序的键值对。如果您关心列顺序，则应该传递要使用的有序键值列表（您也可以使用此列表仅包含一些 dict 条目）：
data = pd.DataFrame({'x': x, 'sin(x)': y}, columns=['x', 'sin(x)'])
回答by dabru
Here's another 1-line solution preserving the specified order, without typing xand sin(x)twice:
这是另一个保留指定顺序的 1-line 解决方案，无需键入x和sin(x)两次：
data = pd.concat([pd.Series(x,name='x'),pd.Series(y,name='sin(x)')], axis=1)
