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:
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?
Someone just recommended creating a dictionary from the data then loading that into the DataFrame like this:
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):
data = pd.DataFrame({'x': x, 'sin(x)': y}, columns=['x', 'sin(x)'])
这是另一个保留指定顺序的1行解决方案,无需输入x
和sin(x)
两次:
data = pd.concat([pd.Series(x,name='x'),pd.Series(y,name='sin(x)')], axis=1)
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