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多个组的数据透视表

pivot-table pivot pandas python

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Georg Heiler · 技术社区 · 7 年前

%pylab inline

import seaborn as sns
sns.set(color_codes=True)

import pandas as pd
import numpy as np
df = pd.DataFrame({"user_id": [1, 2, 3, 4, 5,
                          6, 7, 8, 9],
    "is_sick": [0, 0, 0, 0, 0,
                          0, 1, 1, 1],
                    "sex": ["male", "female", "male", "female", "female",
                          "male", "male", "female", "female"],
                    "age_group": ["young", "old", "old", "young",
                          "small", "old", "young", "young",
                          "old"],
                    "metric_1": [1, 2, 2, 3, 3, 4, 5, 6, 7]})
df['date'] = '2019-01-01'
df['qcut_metric_1'] = pd.qcut(df.metric_1, [0, .25, .5, .66, .75, .97, 1])

# make some more data
df_2 = df.copy()
df_2['date'] = '2019-02-01'
df = pd.concat([df, df_2])

[(sex), (age_group), (sex, age_group)]

注意,我知道 sex 可能类似于:

df['sick_percentage__sex'] = df.groupby(['sex']).is_sick.transform(pd.Series.mean)

一个简单的表可能看起来像:

pd.pivot_table(df, values='sick_percentage__sex', index=['qcut_metric_1', 'sex'], columns=[], aggfunc=np.mean)

看起来像:

        sick_percentage__sex
qcut_metric_1   sex 
(0.999, 2.0]    female  0.40
male    0.25
(2.0, 3.0]  female  0.40
(3.0, 4.28] male    0.25
(4.28, 5.0] male    0.25
(5.0, 6.76] female  0.40
(6.76, 7.0] female  0.40

但是这不适合显示为binned metric( qcut_metric_1 )以及同伙( [(性别),(年龄组),(性别,年龄组)]

所需输出格式:

qcut_metric_1, cohort, percentage_of_sickness

np.mean

agg = df.groupby(['sex']).agg({'user_id':pd.Series.nunique, 'is_sick':pd.Series.mean})
agg.columns = ['unique_users', 'sick_percentage__sex']
df = df.merge(agg, on='sex')

现在为数据透视表的输入提供数据帧。

但现在我也在与加权平均法的语法作斗争:

def wavg(x):
    print(x)
    return np.average(x['sick_percentage__sex'], weights= x['unique_users'])

作为数据透视表 pd.pivot表(df,值=['sick\u percentage\u sex','unique\u users',索引=['qcut\u metric\u 1','sex',列=[],aggfunc=wavg) 只将单个序列(而不是两个序列(值+权重))传递给函数。

0 回复 | 直到 7 年前

1

0

Georg Heiler 7 年前

也许透视表不是解决问题的正确方法。

有没有可能找到更有效的解决办法? 我的输入文件是120G,用于未压缩的CSV/当通过gzip压缩时,3GB的剩余空间将转换为熊猫大约35GB的内存需求。

%pylab inline

import seaborn as sns
sns.set(color_codes=True)

import pandas as pd
import numpy as np
df = pd.DataFrame({"user_id": [1, 2, 3, 4, 5,
                          6, 7, 8, 9],
    "is_sick": [0, 0, 0, 0, 0,
                          0, 1, 1, 1],
                    "sex": ["male", "female", "male", "female", "female",
                          "male", "male", "female", "female"],
                    "age_group": ["young", "old", "old", "young",
                          "small", "old", "young", "young",
                          "old"],
                    "metric_1": [1, 2, 2, 3, 3, 4, 5, 6, 7]})
df['date'] = '2019-01-01'
df['qcut_metric_1'] = pd.qcut(df.metric_1, [0, .25, .5, .66, .75, .97, 1])

# make some more data
df_2 = df.copy()
df_2['date'] = '2019-02-01'
df = pd.concat([df, df_2])
cohorts = [['sex', 'age_group'], ['sex'], ['age_group']]
for cohort in cohorts:
    cohort_name = '_'.join(cohort)
    # print(cohort_name)
    agg = df.groupby(cohort).agg({'user_id':pd.Series.nunique, 'is_sick':pd.Series.mean})
    sick_percentage_column = f'sick_percentage__{cohort_name}'
    agg.columns = ['unique_users', sick_percentage_column]
    merged = df.merge(agg, on=cohort) # INNER (default) JOIN ok, as agg derived from total => no values lost

    groupings = ['qcut_metric_1']
    groupings.extend(cohort)
    result = merged.groupby(groupings).apply(lambda x: np.average(x[sick_percentage_column], weights= x['unique_users'])).reset_index().rename({0:sick_percentage_column}, axis=1)
    display(result)