Pandas 数据框：如何按列中的值分组并从分组值中创建新列
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Pandas dataframe: how to group by values in a column and create new columns out of grouped values
提问by foebu
I have a dataframe with two columns:
我有一个包含两列的数据框：
x y
0 1
1 1
2 2
0 5
1 6
2 8
0 1
1 8
2 4
0 1
1 7
2 3
What I want is:
我想要的是：
x val1 val2 val3 val4
0 1 5 1 1
1 1 6 8 7
2 2 8 4 3
I know that the values in column x are repeated all N times.
我知道 x 列中的值都重复了 N 次。
回答by unutbu
You could use groupby/cumcountto assign column numbers and then call pivot:
您可以使用groupby/cumcount分配列号，然后调用pivot：
import pandas as pd
df = pd.DataFrame({'x': [0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
'y': [1, 1, 2, 5, 6, 8, 1, 8, 4, 1, 7, 3]})
df['columns'] = df.groupby('x')['y'].cumcount()
# x y columns
# 0 0 1 0
# 1 1 1 0
# 2 2 2 0
# 3 0 5 1
# 4 1 6 1
# 5 2 8 1
# 6 0 1 2
# 7 1 8 2
# 8 2 4 2
# 9 0 1 3
# 10 1 7 3
# 11 2 3 3
result = df.pivot(index='x', columns='columns')
print(result)
yields
产量
y
columns 0 1 2 3
x
0 1 5 1 1
1 1 6 8 7
2 2 8 4 3
Or, if you can really rely on the values in xbeing repeated in orderN times,
或者，如果您真的可以依靠按顺序x重复N 次的值，
N = 3
result = pd.DataFrame(df['y'].values.reshape(-1, N).T)
yields
产量
0 1 2 3
0 1 5 1 1
1 1 6 8 7
2 2 8 4 3
Using reshapeis quicker than calling groupby/cumcountand pivot, but it
is less robust since it relies on the values in yappearing in the right order.
使用reshape比调用groupby/cumcountand更快pivot，但它不太健壮，因为它依赖y于以正确顺序出现的值。
