在熊猫中对df.loc应用try / except

Question

我想用try / except在python中应用过滤器。我该怎么做？我的意思是类似于“尝试过滤de csv，如果您不能这样做，因为'ON'或'OFF'不在行df_ok = df中”

import pandas as pd

df = pd.read_csv('a.csv')

try:
    df_ok = df.loc[df['Precio'] != 'ON']
    df_ok = df_ok.loc[df_ok['Precio'] != 'OFF']

csv：

    Espacio País Precio
0   4083496    *     ON
1   4083554    *    OFF
2   4083368    *     ON
3   4083194    *    OFF
4   4083194    *     ON
5   4083194    *    OFF
6   4083194    *     ON
7   4083194    *     ON
8    MARTIN    *    0.5
9    MARTIN    *     89
10   MARTIN    *    198

Answer 1

这是一种方法：

df

Out[110]: 
    Espacio País Precio
0   4083496    *     ON
1   4083554    *    OFF
2   4083368    *     ON
3   4083194    *    OFF
4   4083194    *     ON
5   4083194    *    OFF
6   4083194    *     ON
7   4083194    *     ON
8    MARTIN    *    0.5
9    MARTIN    *     89
10   MARTIN    *    198

df[~df['Precio'].str.contains('ON|OFF')]

Out[111]: 
   Espacio País Precio
8   MARTIN    *    0.5
9   MARTIN    *     89
10  MARTIN    *    198

Answer 2

我认为您可能更适合以下条件：

import pandas as pd

df = pd.read_csv('a.csv')

filtered_df = df[df['Precio']=='ON' | df['Precio']=='OFF']

# Then check if filtered_df is empty

Answer 3

您想要做的是将Precio不是“ ON”和“ OFF”之一的所有行都分隔出来

df[~df['Precio'].isin(['ON','OFF'])]
Out[111]: 
   Espacio País Precio
8   MARTIN    *    0.5
9   MARTIN    *     89
10  MARTIN    *    198