Q: inner join用什么？
# df1, df2 df1.merge(df2, on='xxx', suffixes=('_aaa', '_bbb'))Q: 多个tables拼接如何？
# df1, df2, df3 df1.merge(df2, on=['xxx', 'yyy']) / .merge(df3, on='zzz', suffixes=(...))Q: left join 怎么搞？
df.merge(df2, on='xxx', how='left')Q: right join又怎么搞?
df1.merge(df2, how='right', left_on='l_id', right_on='r_id')Q: outer join是个什么鬼？
df1.merge(df2, on='xxx', how='outer', suffixes=('_x', '_y'))Q: 这个怎么搞？
#先outer iron_1_and_2 = iron_1_actors.merge(iron_2_actors, on='id', how='outer', suffixes=('_1', '_2')) #再找NaN，null m = ((iron_1_and_2['name_1'].isnull()) | (iron_1_and_2['name_2'].isnull())) #filter出null的 print(iron_1_and_2[m].head())Q: semi-join是啥子？anti-join 呢？ 对比与inner join，table并没有合并。 anti-join
# Merge employees and top_cust empl_cust = employees.merge(top_cust, on='srid', how='left', indicator=True) #这个indicator就会显示出该row是来自于left还是right还是both，有一个判别功能。column名为 _merge # Select the srid column where _merge is left_only #选择leftonly就是把没有拼接到table2的项目都找出来了。 srid_list = empl_cust.loc[empl_cust['_merge'] == 'left_only', 'srid'] # Get employees not working with top customers print(employees[employees['srid'].isin(srid_list)])semi-join
# 1st inner join first，找出共性 df_merge = df1.merge(df2, on='id') # filter df_common_list = df1['id'].isin(df_merg['id']) # select df1[df_common_list]Q: 两个tables如何上下拼接？
# df1, df2, df3 df_concat = pd.concat([df1, df2, df3], ingore_index=True)Q: 如何找correlation？
xxx.corr()Q: merge_asof()怎么用 Q: 像使用sql语句的是什么？
xxx.query('name == "aaa" or (age>100 and sex=="male")')Q: id_vars==>是这两个columns不变的情况
