Python Pandas 融化函数
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Pandas Melt Function
提问by slaw
I have a dataframe:
我有一个数据框：
df = pd.DataFrame([[2, 4, 7, 8, 1, 3, 2013], [9, 2, 4, 5, 5, 6, 2014]], columns=['Amy', 'Bob', 'Carl', 'Chris', 'Ben', 'Other', 'Year'])
Amy Bob Carl Chris Ben Other Year
0 2 4 7 8 1 3 2013
1 9 2 4 5 5 6 2014
And a dictionary:
还有一本字典：
d = {'A': ['Amy'], 'B': ['Bob', 'Ben'], 'C': ['Carl', 'Chris']}
I would like to reshape my dataframe to look like this:
我想重塑我的数据框看起来像这样：
Group Name Year Value
0 A Amy 2013 2
1 A Amy 2014 9
2 B Bob 2013 4
3 B Bob 2014 2
4 B Ben 2013 1
5 B Ben 2014 5
6 C Carl 2013 7
7 C Carl 2014 4
8 C Chris 2013 8
9 C Chris 2014 5
10 Other 2013 3
11 Other 2014 6
Note that Otherdoesn't have any values in the Namecolumn and the order of the rows does not matter. I think I should be using the meltfunction but the examples that I've come across aren't too clear.
请注意，列Other中没有任何值，Name行的顺序无关紧要。我想我应该使用该melt函数，但是我遇到的示例不太清楚。
采纳答案by TomAugspurger
meltgets you part way there.
melt让你分道扬镳。
In [29]: m = pd.melt(df, id_vars=['Year'], var_name='Name')
This has everything except Group. To get that, we need to reshape da bit as well.
除了Group. 为了做到这一点，我们还需要d稍微重塑一下。
In [30]: d2 = {}
In [31]: for k, v in d.items():
for item in v:
d2[item] = k
....:
In [32]: d2
Out[32]: {'Amy': 'A', 'Ben': 'B', 'Bob': 'B', 'Carl': 'C', 'Chris': 'C'}
In [34]: m['Group'] = m['Name'].map(d2)
In [35]: m
Out[35]:
Year Name value Group
0 2013 Amy 2 A
1 2014 Amy 9 A
2 2013 Bob 4 B
3 2014 Bob 2 B
4 2013 Carl 7 C
.. ... ... ... ...
7 2014 Chris 5 C
8 2013 Ben 1 B
9 2014 Ben 5 B
10 2013 Other 3 NaN
11 2014 Other 6 NaN
[12 rows x 4 columns]
And moving 'Other' from Nameto Group
并将“其他”从 移动Name到Group
In [8]: mask = m['Name'] == 'Other'
In [9]: m.loc[mask, 'Name'] = ''
In [10]: m.loc[mask, 'Group'] = 'Other'
In [11]: m
Out[11]:
Year Name value Group
0 2013 Amy 2 A
1 2014 Amy 9 A
2 2013 Bob 4 B
3 2014 Bob 2 B
4 2013 Carl 7 C
.. ... ... ... ...
7 2014 Chris 5 C
8 2013 Ben 1 B
9 2014 Ben 5 B
10 2013 3 Other
11 2014 6 Other
[12 rows x 4 columns]
回答by HeadAndTail
Pandas Melt Function :-
熊猫融化功能：-
This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable' and ‘value'.
此函数可用于将 DataFrame 转换为一种格式，其中一列或多列是标识符变量 (id_vars)，而所有其他列，被视为测量变量 (value_vars)，“未旋转”到行轴，只留下两个非标识符列，“变量”和“值”。
eg:-
例如：-
melted = pd.melt(df, id_vars=["weekday"],
var_name="Person", value_name="Score")
we use melt to transform wide data to long data.
我们使用melt 将宽数据转换为长数据。
