pandas :枢轴转换为真/假，删除列 [英] Pandas: Pivot to True/False, drop column

问题描述

我正在尝试创建我认为是简单的数据透视表的表，但是存在严重的问题.我无法做两件事:

I'm trying to create what I think is a simple pivot table but am having serious issues. There are two things I'm unable to do:

1. 删除最后的合作伙伴"列.
2. 如果每个公司都有该伙伴，则将值设置为True或False.

设置:

df = pd.DataFrame({'company':['a','b','c','b'], 'partner':['x','x','y','y'], 'str':['just','some','random','words']})

所需的输出:

company     x      y  
a        True  False
b        True   True
c       False   True

我从开始:

df = df.pivot(values = 'partner', columns = 'partner', index = 'company').reset_index()

这使我很接近，但是当我尝试摆脱伙伴"列时，我什至无法引用它，它也不是索引".

which gets me close, but when I try to get rid of the "partner" column, I can't even reference it, and it's not the "index".

对于第二个问题，我可以使用:

For the second issue, I can use:

df.fillna(False, inplace = True)
df.loc[~(df['x'] == False), 'x'] = True
df.loc[~(df['y'] == False), 'y'] = True

但是这似乎令人难以置信.任何帮助将不胜感激.

but that seems incredibly hacky. Any help would be appreciated.

推荐答案

选项1

df.groupby(['company', 'partner']).size().unstack(fill_value=0).astype(bool)


partner      x      y
company              
a         True  False
b         True   True
c        False   True

摆脱列对象的名称

df.groupby(['company', 'partner']).size().unstack(fill_value=0).astype(bool) \
    .rename_axis(None, 1).reset_index()

  company      x      y
0       a   True  False
1       b   True   True
2       c  False   True

选项2

pd.crosstab(df.company, df.partner).astype(bool)

partner      x      y
company              
a         True  False
b         True   True
c        False   True


pd.crosstab(df.company, df.partner).astype(bool) \
    .rename_axis(None, 1).reset_index()

  company      x      y
0       a   True  False
1       b   True   True
2       c  False   True

选项3

f1, u1 = pd.factorize(df.company.values)
f2, u2 = pd.factorize(df.partner.values)
n, m = u1.size, u2.size

b = np.bincount(f1 * m + f2)
pad = np.zeros(n * m - b.size, dtype=int)
b = np.append(b, pad)
v = b.reshape(n, m).astype(bool)

pd.DataFrame(np.column_stack([u1, v]), columns=np.append('company', u2))

  company      x      y
0       a   True  False
1       b   True   True
2       c  False   True

---

计时
小数据

---

Timing
small data

%timeit df.groupby(['company', 'partner']).size().unstack(fill_value=0).astype(bool).rename_axis(None, 1).reset_index()
%timeit pd.crosstab(df.company, df.partner).astype(bool).rename_axis(None, 1).reset_index()

%%timeit
f1, u1 = pd.factorize(df.company.values)
f2, u2 = pd.factorize(df.partner.values)
n, m = u1.size, u2.size

b = np.bincount(f1 * m + f2)
pad = np.zeros(n * m - b.size, dtype=int)
b = np.append(b, pad)
v = b.reshape(n, m).astype(bool)

pd.DataFrame(np.column_stack([u1, v]), columns=np.append('company', u2))

1000 loops, best of 3: 1.67 ms per loop
100 loops, best of 3: 5.97 ms per loop
1000 loops, best of 3: 301 µs per loop

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