pandas将一些列转换为行

So my dataset has some information by location for n dates. The problem is each date is actually a different column header. For example the CSV looks like

所以我的数据集有n个日期的位置信息。问题是每个日期实际上是一个不同的列标题。例如,CSV看起来像

location    name    Jan-2010    Feb-2010    March-2010
A           "test"  12          20          30
B           "foo"   18          20          25

What I would like is for it to look like

我希望它看起来像

location    name    Date        Value
A           "test"  Jan-2010    12       
A           "test"  Feb-2010    20
A           "test"  March-2010  30
B           "foo"   Jan-2010    18       
B           "foo"   Feb-2010    20
B           "foo"   March-2010  25

problem is I don't know how many dates are in the column (though I know they will always start after name)

问题是我不知道列中有多少个日期(虽然我知道它们总是会在名字后面开始)

2 个解决方案

#1

You can use pd.melt to get most of the way there, and then sort:

您可以使用pd.melt来获取大部分内容,然后排序:

>>> df
  location  name  Jan-2010  Feb-2010  March-2010
0        A  test        12        20          30
1        B   foo        18        20          25
>>> df2 = pd.melt(df, id_vars=["location", "name"], 
                  var_name="Date", value_name="Value")
>>> df2
  location  name        Date  Value
0        A  test    Jan-2010     12
1        B   foo    Jan-2010     18
2        A  test    Feb-2010     20
3        B   foo    Feb-2010     20
4        A  test  March-2010     30
5        B   foo  March-2010     25
>>> df2 = df2.sort(["location", "name"])
>>> df2
  location  name        Date  Value
0        A  test    Jan-2010     12
2        A  test    Feb-2010     20
4        A  test  March-2010     30
1        B   foo    Jan-2010     18
3        B   foo    Feb-2010     20
5        B   foo  March-2010     25

(Might want to throw in a .reset_index(drop=True), just to keep the output clean.)

(可能想要输入.reset_index(drop = True),只是为了保持输出清洁。)

Note: pd.DataFrame.sort has been deprecated in favour of pd.DataFrame.sort_values.

注意:不推荐使用pd.DataFrame.sort,而选择pd.DataFrame.sort_values。

#2

I guess I found a simpler solution

我想我找到了一个更简单的解决方案

temp1 = pd.melt(df1, id_vars=["location"], var_name='Date', value_name='Value')
temp2 = pd.melt(df1, id_vars=["name"], var_name='Date', value_name='Value')

Concat whole temp1 with temp2's column name

concat整个temp1与temp2的列名

temp1['new_column'] = temp2['name']

You now have what you asked for.

你现在拥有了你所要求的。

#1