从pandas DataFrame删除包含空单元格的行 [英] Drop rows containing empty cells from a pandas DataFrame

问题描述

我有一个pd.DataFrame，它是通过解析一些excel电子表格创建的.一列的单元格为空.例如，下面是该列的频率输出，有32320条记录缺少 Tenant 的值.

I have a pd.DataFrame that was created by parsing some excel spreadsheets. A column of which has empty cells. For example, below is the output for the frequency of that column, 32320 records have missing values for Tenant.

>>> value_counts(Tenant, normalize=False)
                              32320
    Thunderhead                8170
    Big Data Others            5700
    Cloud Cruiser              5700
    Partnerpedia               5700
    Comcast                    5700
    SDP                        5700
    Agora                      5700
    dtype: int64

我正在尝试删除缺少租户的行，但是.isnull()选项无法识别丢失的值.

I am trying to drop rows where Tenant is missing, however .isnull() option does not recognize the missing values.

>>> df['Tenant'].isnull().sum()
    0

该列的数据类型为对象".在这种情况下会发生什么?如何在缺少 Tenant 的地方删除记录?

The column has data type "Object". What is happening in this case? How can I drop records where Tenant is missing?

推荐答案

如果熊猫是np.nan对象，则它将识别为空值，该对象将在DataFrame中打印为NaN.您缺少的值可能是空字符串，Pandas不能将其识别为null.要解决此问题，您可以使用replace()将空字符串(或空单元格中的任何东西)转换为np.nan对象，然后在DataFrame上调用dropna()删除具有空租户的行.

Pandas will recognise a value as null if it is a np.nan object, which will print as NaN in the DataFrame. Your missing values are probably empty strings, which Pandas doesn't recognise as null. To fix this, you can convert the empty stings (or whatever is in your empty cells) to np.nan objects using replace(), and then call dropna()on your DataFrame to delete rows with null tenants.

为了演示，我们在Tenants列中创建一个带有一些随机值和一些空字符串的DataFrame:

To demonstrate, we create a DataFrame with some random values and some empty strings in a Tenants column:

>>> import pandas as pd
>>> import numpy as np
>>> 
>>> df = pd.DataFrame(np.random.randn(10, 2), columns=list('AB'))
>>> df['Tenant'] = np.random.choice(['Babar', 'Rataxes', ''], 10)
>>> print df

          A         B   Tenant
0 -0.588412 -1.179306    Babar
1 -0.008562  0.725239         
2  0.282146  0.421721  Rataxes
3  0.627611 -0.661126    Babar
4  0.805304 -0.834214         
5 -0.514568  1.890647    Babar
6 -1.188436  0.294792  Rataxes
7  1.471766 -0.267807    Babar
8 -1.730745  1.358165  Rataxes
9  0.066946  0.375640         

现在，我们将Tenants列中的所有空字符串替换为np.nan对象，如下所示:

Now we replace any empty strings in the Tenants column with np.nan objects, like so:

>>> df['Tenant'].replace('', np.nan, inplace=True)
>>> print df

          A         B   Tenant
0 -0.588412 -1.179306    Babar
1 -0.008562  0.725239      NaN
2  0.282146  0.421721  Rataxes
3  0.627611 -0.661126    Babar
4  0.805304 -0.834214      NaN
5 -0.514568  1.890647    Babar
6 -1.188436  0.294792  Rataxes
7  1.471766 -0.267807    Babar
8 -1.730745  1.358165  Rataxes
9  0.066946  0.375640      NaN

现在我们可以删除空值:

Now we can drop the null values:

>>> df.dropna(subset=['Tenant'], inplace=True)
>>> print df

          A         B   Tenant
0 -0.588412 -1.179306    Babar
2  0.282146  0.421721  Rataxes
3  0.627611 -0.661126    Babar
5 -0.514568  1.890647    Babar
6 -1.188436  0.294792  Rataxes
7  1.471766 -0.267807    Babar
8 -1.730745  1.358165  Rataxes

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