熊猫可以用列作为索引吗?

I have a spreadsheet like this:

我有这样一个电子表格:

Locality    2005    2006    2007    2008    2009

ABBOTSFORD  427000  448000  602500  600000  638500
ABERFELDIE  534000  600000  735000  710000  775000
AIREYS INLET459000  440000  430000  517500  512500

I don't want to manually swap the column with the row. Could it be possible to use pandas reading data to a list as this:

我不想手动地将列与行交换。是否有可能使用熊猫读取数据的清单如下:

data['ABBOTSFORD']=[427000,448000,602500,600000,638500]
data['ABERFELDIE']=[534000,600000,735000,710000,775000]
data['AIREYS INLET']=[459000,440000,430000,517500,512500]

2 个解决方案

#1

Yes, with set_index you can make Locality your row index.

是的，有了set_index，您就可以创建本地行索引。

data.set_index('Locality', inplace=True)

If inplace=True is not provided, set_index returns the modified dataframe as a result.

如果不提供inplace=True，则set_index返回修改后的dataframe。

Example:

例子:

> import pandas as pd
> df = pd.DataFrame([['ABBOTSFORD', 427000, 448000],
                     ['ABERFELDIE', 534000, 600000]],
                    columns=['Locality', 2005, 2006])

> df
     Locality    2005    2006
0  ABBOTSFORD  427000  448000
1  ABERFELDIE  534000  600000

> df.set_index('Locality', inplace=True)
> df
              2005    2006
Locality                  
ABBOTSFORD  427000  448000
ABERFELDIE  534000  600000

> df.loc['ABBOTSFORD']
2005    427000
2006    448000
Name: ABBOTSFORD, dtype: int64

> df.loc['ABBOTSFORD'][2005]
427000

> df.loc['ABBOTSFORD'].values
array([427000, 448000])

> df.loc['ABBOTSFORD'].tolist()
[427000, 448000]

#2

You can change the index as explained already using set_index. You don't need to manually swap rows with columns, there is a transpose (data.T) method in pandas that does it for you:

可以使用set_index更改索引，如前面介绍的那样。你不需要手动地用列交换行，熊猫中有一个转置(data.T)方法可以帮你:

> df = pd.DataFrame([['ABBOTSFORD', 427000, 448000],
                    ['ABERFELDIE', 534000, 600000]],
                    columns=['Locality', 2005, 2006])

> newdf = df.set_index('Locality').T
> newdf

Locality    ABBOTSFORD  ABERFELDIE
2005        427000      534000
2006        448000      600000

then you can fetch the dataframe column values and transform them to a list:

然后您可以获取dataframe列值并将它们转换为列表:

> newdf['ABBOTSFORD'].values.tolist()

[427000, 448000]

#1