Pandas：以“列”标题作为行元素读取时间序列数据的 CSV
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Pandas: Read a CSV of timeseries data with 'column' header as row element
提问by Steve Pike
Is it possible to read a CSV file in this format:
是否可以读取这种格式的 CSV 文件：
2013-01-01,A,1
2013-01-02,A,2
2013-01-03,A,3
2013-01-04,A,4
2013-01-05,A,5
2013-01-01,B,1
2013-01-02,B,2
2013-01-03,B,3
2013-01-04,B,4
2013-01-05,B,5
into a DataFrame that ends up like this:
进入一个像这样结束的数据帧：
A B
2013-01-01 1 1
2013-01-02 2 2
2013-01-03 3 3
2013-01-04 4 4
2013-01-05 5 5
I couldn't see anything in the I/O docs (http://pandas.pydata.org/pandas-docs/dev/io.html)
我在 I/O 文档（http://pandas.pydata.org/pandas-docs/dev/io.html）中看不到任何内容
回答by Andy Hayden
Why not reshape (pivot) afteryou've read in the DataFrame?
读完 DataFrame后，为什么不重塑（枢轴）？
In [1]: df = pd.read_csv('foo.csv', sep=',', parse_dates=[0], header=None,
names=['Date', 'letter', 'value'])
In [2]: df
Out[2]:
Date letter value
0 2013-01-01 00:00:00 A 1
1 2013-01-02 00:00:00 A 2
2 2013-01-03 00:00:00 A 3
3 2013-01-04 00:00:00 A 4
4 2013-01-05 00:00:00 A 5
5 2013-01-01 00:00:00 B 1
6 2013-01-02 00:00:00 B 2
7 2013-01-03 00:00:00 B 3
8 2013-01-04 00:00:00 B 4
9 2013-01-05 00:00:00 B 5
In [3]: df.pivot(index='Date', columns='letter', values='value')
Out[3]:
letter A B
Date
2013-01-01 1 1
2013-01-02 2 2
2013-01-03 3 3
2013-01-04 4 4
2013-01-05 5 5
