'two','one','three'],'C':['small','large','large','small','small','large','small','small'],'D':[1,2,2,3,3,4,5,6]}df=pd.DataFrame(data)# 使用自定义函数进行去重计数defmy_nunique(series):returnseries.nunique()result=df.agg(
AI代码解释 selectsubstr(a.ts,1,10)asdt,count(distinct a.uid),count(distinctif(datediff(substr(b.ts,1,10),substr(a.ts,1,10))=1,b.uid,null))as1_day_remain_uid,count(distinctif(datediff(substr(b.ts,1,10),substr(a.ts,1,10))=6,b.uid,null))as7_day_remain_uid,count(distinctif...
2.2 结合GroupBy使用 Count Distinct操作经常与GroupBy结合使用,以计算每个组内的不重复值数量: importpandasaspd# 创建示例数据data={'category':['A','B','A','B','C','A','B'],'product':['X','Y','Z','X','Y','X','Z'],'customer':['C1','C2','C3','C1','C2','C4','C3']}...
[25, 30, 35, 40, 45], 'City': ['New York', 'Paris', 'London', 'Tokyo', 'Sydney']} df = pd.DataFrame(data) # 使用groupby按照Name进行分组,并将City列的值拆分 def split_values(x): return pd.Series(x['City'].split()) result = df.groupby('Name').apply(split_values)...
col,n=5): return smoker.sort_values(by=col)[-n:] df1.groupby('smoker').apply(top,col...
total = df.get_value(df.loc[df['tip'] ==1.66].index.values[0],'total_bill') distinct drop_duplicates根据某列对dataframe进行去重: df.drop_duplicates(subset=['sex'], keep='first', inplace=True) 包含参数: subset,为选定的列做distinct,默认为所有列; ...
index.get_level_values(2).unique() # 去掉为零小数,12.00 -> 12 df.astype('str').applymap(lambda x: x.replace('.00', '')) # 插入数据,在第三列加入「两倍」列 df.insert(3, '两倍', df['值']*2) # 枚举转换 df['gender'] = df.gender.map({'male':'男', 'female':'女'}) ...
Unique Values, Value Counts, and Membership isin Compute boolean array indicating whether each Series value is contained in the passed sequence of values match Compute integer indices for each value in an array into another array of distinct values; helpful for data alignment and join-type operation...
total = df.loc[df['tip'] ==1.66,'total_bill'].values[0]total = df.get_value(df.loc[df['tip'] ==1.66].index.values[0],'total_bill') distinct drop_duplicates根据某列对dataframe进行去重: df.drop_duplicates(subset=['sex'], keep='first', inplace=True) ...
total = df.get_value(df.loc[df['tip'] ==1.66].index.values[0],'total_bill') distinct drop_duplicates根据某列对dataframe进行去重: df.drop_duplicates(subset=['sex'], keep='first', inplace=True) 包含参数: subset,为选定的列做distinct,默认为所有列; ...