import pandas as pd
import numpy as np
from tabulate import tabulate
pandas dataframe boolean条件数据筛选3
df=pd.read_csv('data/stackoverflow_qa.csv')
dd=df.head()
print(tabulate(dd,headers=df.columns,tablefmt='pipe'))
| | id | creationdate | score | viewcount | title | answercount | commentcount | favoritecount | quest_name | quest_rep | ans_name | ans_rep |
|---:|--------:|:--------------------|--------:|------------:|:------------------------------------------------------------------------------------|--------------:|---------------:|----------------:|:---------------|------------:|:----------------|----------:|
| 0 | 5486226 | 2011-03-30 12:26:50 | 4 | 2113 | Rolling median in python | 3 | 4 | 1 | yueerhu | 125 | Mike Pennington | 26995 |
| 1 | 5515021 | 2011-04-01 14:50:44 | 8 | 7015 | Compute a compounded return series in Python | 3 | 6 | 7 | Jason Strimpel | 3301 | Mike Pennington | 26995 |
| 2 | 5558607 | 2011-04-05 21:13:50 | 2 | 7392 | Sort a pandas DataMatrix in ascending order | 2 | 0 | 1 | Jason Strimpel | 3301 | Wes McKinney | 43310 |
| 3 | 6467832 | 2011-06-24 12:31:45 | 9 | 13056 | How to get the correlation between two timeseries using Pandas | 1 | 0 | 7 | user814005 | 117 | Wes McKinney | 43310 |
| 4 | 7577546 | 2011-09-28 01:58:38 | 9 | 2488 | Using pandas, how do I subsample a large DataFrame by group in an efficient manner? | 1 | 0 | 5 | Uri Laserson | 958 | HYRY | 54137 |
- 列与列之间的比较形成的筛选条件
- 筛选出 answercount > score 的数据 如下
dd=df.loc[df['answercount']>df['score']].head()
print(tabulate(dd,headers=df.columns,tablefmt='pipe'))
| | id | creationdate | score | viewcount | title | answercount | commentcount | favoritecount | quest_name | quest_rep | ans_name | ans_rep |
|---:|---------:|:--------------------|--------:|------------:|:-------------------------------------------------------------------------|--------------:|---------------:|----------------:|:-----------------|------------:|:-----------------------|----------:|
| 10 | 8273092 | 2011-11-25 18:39:02 | 1 | 2333 | python: pandas install errors | 2 | 0 | nan | codingknob | 2279 | codingknob | 2279 |
| 46 | 9927711 | 2012-03-29 14:42:42 | 1 | 1659 | Reading csv in python pandas and handling bad values | 3 | 0 | 2 | Dave31415 | 914 | eumiro | 104313 |
| 54 | 10003171 | 2012-04-03 23:59:41 | 1 | 404 | What is an efficient way in pandas to do summaryBy(...,full.dimension=T) | 2 | 1 | nan | LmW. | 486 | Wes McKinney | 43310 |
| 59 | 10027719 | 2012-04-05 11:28:00 | 0 | 500 | Installing Pandas with Python 2.5 on Windows | 1 | 0 | nan | JamesS | 191 | Wes McKinney | 43310 |
| 77 | 10393447 | 2012-05-01 04:12:13 | 0 | 130 | Scope gotcha when dynamically adding methods in a loop | 2 | 0 | nan | Chris Billington | 424 | Ignacio Vazquez-Abrams | 513959 |
- iloc 中不能使用类似 df['answercount'] >2 的条件 来看一下原因
s = df['answercount'] >2
s.head()
0 True
1 True
2 False
3 False
4 False
Name: answercount, dtype: bool
type(s)
pandas.core.series.Series
上列中df['answercount'] >2返回的Series类型，index跟loc使用的index是一致的，
而iloc使用的是下标索引
看下效果,会报错
df.iloc[s]
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-10-9ddb204bb531> in <module>()
----> 1 df.iloc[s]
~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1476
1477 maybe_callable = com._apply_if_callable(key, self.obj)
-> 1478 return self._getitem_axis(maybe_callable, axis=axis)
1479
1480 def _is_scalar_access(self, key):
~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
2084
2085 if com.is_bool_indexer(key):
-> 2086 self._validate_key(key, axis)
2087 return self._getbool_axis(key, axis=axis)
2088
~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py in _validate_key(self, key, axis)
1945 if hasattr(key, 'index') and isinstance(key.index, Index):
1946 if key.index.inferred_type == 'integer':
-> 1947 raise NotImplementedError("iLocation based boolean "
1948 "indexing on an integer type "
1949 "is not available")
NotImplementedError: iLocation based boolean indexing on an integer type is not available
可以只使用series的values，如下
s.values
array([ True, True, False, ..., False, False, False])
dd=df.iloc[s.values].head()
print(tabulate(dd,headers=df.columns,tablefmt='pipe'))
| | id | creationdate | score | viewcount | title | answercount | commentcount | favoritecount | quest_name | quest_rep | ans_name | ans_rep |
|---:|--------:|:--------------------|--------:|------------:|:---------------------------------------------------------------------------|--------------:|---------------:|----------------:|:---------------|------------:|:----------------|----------:|
| 0 | 5486226 | 2011-03-30 12:26:50 | 4 | 2113 | Rolling median in python | 3 | 4 | 1 | yueerhu | 125 | Mike Pennington | 26995 |
| 1 | 5515021 | 2011-04-01 14:50:44 | 8 | 7015 | Compute a compounded return series in Python | 3 | 6 | 7 | Jason Strimpel | 3301 | Mike Pennington | 26995 |
| 7 | 7813132 | 2011-10-18 20:16:12 | 10 | 18917 | Convert array of string (category) to array of int from a pandas dataframe | 3 | 0 | 6 | Jean-Pat | 882 | Wes McKinney | 43310 |
| 8 | 7837722 | 2011-10-20 14:46:14 | 201 | 223746 | What is the most efficient way to loop through dataframes with pandas? | 8 | 3 | 115 | Muppet | 1563 | Nick Crawford | 2779 |
| 12 | 8842114 | 2012-01-12 20:52:41 | 8 | 4009 | How to apply slicing on pandas Series of strings | 3 | 2 | 1 | davidbrai | 934 | Rob Wouters | 10083 |
dd=df.iloc[s.values,[1,3,4]].head()
print(tabulate(dd,headers=df.columns,tablefmt='pipe'))
| id | creationdate | score | viewcount |
|-----:|:--------------------|--------:|:---------------------------------------------------------------------------|
| 0 | 2011-03-30 12:26:50 | 2113 | Rolling median in python |
| 1 | 2011-04-01 14:50:44 | 7015 | Compute a compounded return series in Python |
| 7 | 2011-10-18 20:16:12 | 18917 | Convert array of string (category) to array of int from a pandas dataframe |
| 8 | 2011-10-20 14:46:14 | 223746 | What is the most efficient way to loop through dataframes with pandas? |
| 12 | 2012-01-12 20:52:41 | 4009 | How to apply slicing on pandas Series of strings |
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