source:https://stackoverflow.com/questions/41620920/groupby-conditional-sum-of-adjacent-rows-pandas 数据片段如下: duration location user 0 10 house A 1 5 house A 2 5 gym A 3 4 gym B 4 10 shop B 5 4 gym B 6 6 gym B 按照user 分组后,各组当 location 连续相同时对 duration 进行求和,...
Thegroupby('Category').filter()filters groups where the sum of 'Values' is greater than 100. This is useful for conditional analysis. GroupBy: Sort Groups This example demonstrates how to sort groups based on a column. groupby_sort.py import polars as pl data = { 'Category': ['A', 'B...
我想对熊猫做一个条件聚合,但是有两个条件,我已经看到了这个Python Pandas Conditional Sum with Groupby,我发现非常有用,但是如果我添加另一个条件,例如: g.apply(lambda x: x[x[x['key2'] == 'one']['data2']<0.4]['data1'].sum()),即添加一个条件,我想对Key2等于1且data2小于0.4的那些求和 ...
、 我想对熊猫做一个条件聚合,但是有两个条件,我已经看到了这个Python Pandas Conditional Sum with Groupby,我发现非常有用,但是如果我添加另一个条件,例如: g.apply(lambda x: x[x[x['key2'] == 'one']['data2']<0.4]['data1'].sum()),即添加一个条件,我想对Key2等于1且data2小于0.4的那些<e...
group_and_sum_df=concat_df.groupby(by='组合名称')['余额'].sum() # 去重,保留唯一数据行 drop_duplicate_df=concat_df.drop_duplicates(subset=['组合名称'], keep='first') # 合并分组表和去重表 merge_df=pd.merge(drop_duplicate_df, group_and_sum_df, how='left', on='组合名称') ...
让我们定义一个简单的辅助函数: def distribute(g): nans = g['value_KPI'].isna() g.loc[~nans, 'Weightages'] += g.loc[nans, 'Weightages'].sum()/sum(~nans) g.loc[nans, 'Weightages'] = 'NaN' return g 现在我们在groupby之后将其应用于每个组 df.groupby(['Groups']).apply(distr...
importnumpyasnpimportpandasaspddefconditional_entropy(y,x):# 计算 P(X) 和 P(Y|X)p_x=x.value_counts(normalize=True)p_y_given_x=y.groupby(x).value_counts(normalize=True).unstack().fillna(0)# 计算条件熵 H(Y|X)h_y_given_x=0.0forx_valinp_x.index:fory_valinp_y_given_x.columns...
df_data_hour = df_data.groupby( pd.Grouper(key='datetime', axis=0, freq='H')).mean() df_labels_hour = df_labels.groupby( pd.Grouper(key='datetime', axis=0, freq='H')).sum() for name in df.columns: if name not in ['datetime', 'machine_status']: fig, axs = plt.subplots...
over("y").alias("avg_a_by_type"), #over()实现分组聚合而且又没有groupby那样伤害分组列,这里over的执行顺序在mean之前确保分组后再reduction pl.col("b").mean().over(["x", "y"]).alias("avg_a_by_type_combination"), #partition by多列 ) 7.按行操作fold() out = df.filter( pl.fold(...
(self, df): self.df = df def calculate_conditional_probability(self, condition_column, target_column): return self.df.groupby(condition_column)[target_column].value_counts(normalize=True).unstack() # 示例 calculator = ProbabilityCalculator(df) cond_prob = calculator.calculate_conditional_...