如何用最少的代码创建过滤后的 DataFrame

Posted 2023-03-12

【中文标题】如何用最少的代码创建过滤后的 DataFrame

【英文标题】：How to create filtered DataFrame with minimum code

【发布时间】：2017-01-07 12:36:30

【问题描述】：

有四辆车：bmw、geo、vw 和porsche：

import pandas as pd
df = pd.DataFrame(
    'car':      ['bmw','geo','vw','porsche'],
    'warranty': ['yes','yes','yes','no'], 
    'dvd':      ['yes','yes','no','yes'], 
    'sunroof':  ['yes','no','no','no'])

我想创建一个过滤后的 DataFrame，其中仅列出具有所有三个功能的汽车：DVD 播放器、天窗和保修（我们知道这里是 BMW，所有功能都设置为“是”）。

我可以一次做一列：

cars_with_warranty = df['car'][df['warranty']=='yes']
print(cars_with_warranty)

然后我需要对dvd和天窗柱进行类似的列计算：

cars_with_dvd = df['car'][df['dvd']=='yes']
cars_with_sunroof = df['car'][df['sunroof']=='yes']

我想知道是否有一种巧妙的方法可以创建过滤后的DataFrame？

稍后编辑：

发布的解决方案效果很好。但生成的cars_with_all_three 是一个简单的列表变量。我们需要 DataFrame 对象，其中只有一辆“bmw”汽车作为其唯一的行和所有三列：dvd、天窗和保修（所有三个值都设置为“yes”）。

cars_with_all_three = []
for ind, car in enumerate(df['car']):
    if df['dvd'][ind] == df['warranty'][ind] == df['sunroof'][ind] == 'yes':
        cars_with_all_three.append(car)

【参考方案1】：

您可以使用简单的loop 和enumerate：

cars_with_all_three = []
for ind, car in enumerate(df['car']):
    if df['dvd'][ind] == df['warranty'][ind] == df['sunroof'][ind] == 'yes':
        cars_with_all_three.append(car)

如果您执行print(cars_with_all_three)，您将获得['bmw']。

或者，如果你想变得非常聪明并使用单线，你可以这样做：

[car for ind, car in enumerate(df['car']) if df['dvd'][ind] == df['warranty'][ind] == df['sunroof'][ind] == 'yes']

希望对你有帮助

【参考方案2】：

你可以使用boolean indexing:

print ((df.dvd == 'yes') & (df.sunroof == 'yes') & (df.warranty == 'yes'))
0     True
1    False
2    False
3    False
dtype: bool

print (df[(df.dvd == 'yes') & (df.sunroof == 'yes') & (df.warranty == 'yes')])
   car  dvd sunroof warranty
0  bmw  yes     yes      yes

#if need filter only column 'car' 
print (df.ix[(df.dvd == 'yes')&(df.sunroof == 'yes')&(df.warranty == 'yes'), 'car'])
0    bmw
Name: car, dtype: object

另一种解决方案是检查列中的所有值是否为yes，然后通过all 检查所有值是否为True：

print ((df[[ u'dvd', u'sunroof', u'warranty']] == "yes").all(axis=1))
0     True
1    False
2    False
3    False
dtype: bool

print (df[(df[[ u'dvd', u'sunroof', u'warranty']] == "yes").all(axis=1)])
   car  dvd sunroof warranty
0  bmw  yes     yes      yes

print (df.ix[(df[[ u'dvd', u'sunroof', u'warranty']] == "yes").all(axis=1), 'car'])
0    bmw
Name: car, dtype: object

使用最少代码的解决方案，如果 DataFrame 只有 4 列，如示例：

print (df[(df.set_index('car') == 'yes').all(1).values])
   car  dvd sunroof warranty
0  bmw  yes     yes      yes

时间安排：

In [44]: %timeit ([car for ind, car in enumerate(df['car']) if df['dvd'][ind] == df['warranty'][ind] == df['sunroof'][ind] == 'yes'])
10 loops, best of 3: 120 ms per loop

In [45]: %timeit (df[(df.dvd == 'yes')&(df.sunroof == 'yes')&(df.warranty == 'yes')])
The slowest run took 4.39 times longer than the fastest. This could mean that an intermediate result is being cached.
100 loops, best of 3: 2.09 ms per loop

In [46]: %timeit (df[(df[[ u'dvd', u'sunroof', u'warranty']] == "yes").all(axis=1)])
1000 loops, best of 3: 1.53 ms per loop

In [47]: %timeit (df[(df.ix[:, [u'dvd', u'sunroof', u'warranty']] == "yes").all(axis=1)])
The slowest run took 4.46 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 1.51 ms per loop

In [48]: %timeit (df[(df.set_index('car') == 'yes').all(1).values])
1000 loops, best of 3: 1.64 ms per loop

In [49]: %timeit (mer(df))
The slowest run took 4.17 times longer than the fastest. This could mean that an intermediate result is being cached.
100 loops, best of 3: 3.85 ms per loop

计时码：

df = pd.DataFrame(
    'car':      ['bmw','geo','vw','porsche'],
    'warranty': ['yes','yes','yes','no'], 
    'dvd':      ['yes','yes','no','yes'], 
    'sunroof':  ['yes','no','no','no'])

print (df)
df = pd.concat([df]*1000).reset_index(drop=True)

def mer(df):
    df = df.set_index('car')
    return df[df[[ u'dvd', u'sunroof', u'warranty']] == "yes"].dropna().reset_index()

【参考方案3】：

试试这个：

df = df.set_index('car')
df[df[[ u'dvd', u'sunroof', u'warranty']] == "yes"].dropna().reset_index()

 df
   car  dvd sunroof warranty
0  bmw  yes     yes      yes


df = df.set_index('car')
df[df[[ u'dvd', u'sunroof', u'warranty']]== "yes"].dropna().index.values

['bmw']