Python Pandas Concat"WHERE"满足条件 [英] Python Pandas Concat "WHERE" a Condition is met

问题描述

如何从许多Python Pandas数据框中连接"特定的列，而在许多数据框中的每个列中的另一列都满足特定条件(在这里俗称条件"X").

在SQL中，使用带有WHERE df2.Col2 ="X"和df3.Col2 ="X"和df4.col2 ="X" ...等(可以动态运行)的JOIN子句将很简单./p>

在我的情况下，我想创建一个大数据框，其中包含来自多个数据框中每个数据框的所有"Col1"，但仅包含Col1行值，而相应的Col2行值大于"0.8".如果不满足此条件，则Col1值应为"NaN".

任何想法都将最有帮助！预先感谢！

解决方案

考虑 pd.DataFrame s 列表 dfs >

 将pandas导入为pd将numpy导入为npnp.random.seed([3,1415])dfs = [pd.DataFrame(np.random.rand(10，2)，column = ['Col1'，'Col2'])表示_ in range(5)] 

我将使用 pd.concat 加入

原始连接
堆积值，而不管它来自何处

  pd.concat([dfs中d的[d.Col1.loc [d.Col2.gt(.8)] for d]，ignore_index = True)0 0.8504451 0.9348292 0.8798913 0.0858234 0.7396355 0.7005666 0.5423297 0.8820298 0.4962509 0.58530910 0.883372名称:Col1，dtype:float64 

加入源信息
使用 keys 参数

  pd.concat([dfs中d的[d.Col1.loc [d.Col2.gt(.8)]]，keys = range(len(dfs)))0 3 0.8504455 0.9348296 0.8798911 1 0.0858232 0.7396357 0.7005662 4 0.5423293 3 0.8820294 0.4962508 0.5853094 0 0.883372名称:Col1，dtype:float64 

另一种方法
使用 query

  pd.concat([d.query('Col2> .8').dol中d的col1]，keys = range(len(dfs)))0 3 0.8504455 0.9348296 0.8798911 1 0.0858232 0.7396357 0.7005662 4 0.5423293 3 0.8820294 0.4962508 0.5853094 0 0.883372名称:Col1，dtype:float64 

How can I "concat" a specific column from many Python Pandas dataframes, WHERE another column in each of the many dataframes meets a certain condition (colloquially termed condition "X" here).

In SQL this would be simple using JOIN clause with WHERE df2.Col2 = "X" and df3.Col2 = "X" and df4.col2 = "X"... etc (which can be run dynamically).

In my case, I want to create a big dataframe with all the "Col1"s from each of the many dataframes, but only include the Col1 row values WHERE the corresponding Col2 row value is greater than "0.8". When this condition isn't met, the Col1 value should be "NaN".

Any ideas would be most helpful! Thanks in advance!

解决方案

consider the list dfs of pd.DataFrames

import pandas as pd
import numpy as np


np.random.seed([3,1415])
dfs = [pd.DataFrame(np.random.rand(10, 2),
                    columns=['Col1', 'Col2']) for _ in range(5)]

I'll use pd.concat to join

raw concat
stack values without regard to where it came from

pd.concat([d.Col1.loc[d.Col2.gt(.8)] for d in dfs], ignore_index=True)

0     0.850445
1     0.934829
2     0.879891
3     0.085823
4     0.739635
5     0.700566
6     0.542329
7     0.882029
8     0.496250
9     0.585309
10    0.883372
Name: Col1, dtype: float64

join with source information
use the keys parameter

pd.concat([d.Col1.loc[d.Col2.gt(.8)] for d in dfs], keys=range(len(dfs)))

0  3    0.850445
   5    0.934829
   6    0.879891
1  1    0.085823
   2    0.739635
   7    0.700566
2  4    0.542329
3  3    0.882029
   4    0.496250
   8    0.585309
4  0    0.883372
Name: Col1, dtype: float64

another approach
use query

pd.concat([d.query('Col2 > .8').Col1 for d in dfs], keys=range(len(dfs)))

0  3    0.850445
   5    0.934829
   6    0.879891
1  1    0.085823
   2    0.739635
   7    0.700566
2  4    0.542329
3  3    0.882029
   4    0.496250
   8    0.585309
4  0    0.883372
Name: Col1, dtype: float64

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