Python Pandas：过滤数据框
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Python Pandas: Filtering a data frame
提问by kk415kk
I'm pretty new to Pandas but wanted to try it out after working with R for a while.
我对 Pandas 还很陌生，但在使用 R 一段时间后想尝试一下。
A problem I'm having is figuring out why a filter isn't working for one of my data frames. I have a data frame data_dfwith multiple columns, one of which is cwhich holds country names. I'm trying to filter out the rows where c == None.
我遇到的一个问题是弄清楚为什么过滤器对我的一个数据框不起作用。我有一个data_df包含多列的数据框，其中一列c包含国家/地区名称。我正在尝试过滤掉c == None.
My first attempt was to do this:
我的第一次尝试是这样做：
countries_df = data_df[data_df.c != None]
However, that yielded 0 rows. This, however, worked:
但是，这产生了 0 行。然而，这奏效了：
countries_df = data_df[~data_df.c.isin([None])]
Can someone explain why? It seems that from the Pandas doc, the first should be able to filter correctly.
有人可以解释为什么吗？从 Pandas 文档看来，第一个应该能够正确过滤。
Some sample rows:
一些示例行：
_heartbeat_ a al c cy g
0 NaN Mozilla/5.0 (Linux; U; Android 4.1.2; en-us; H... en-US US Anaheim 15r91
1 NaN Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ... en-us None NaN ifIpBW
2 NaN Mozilla/5.0 (Windows NT 6.1; rv:21.0) Gecko/20... en-US,en;q=0.5 US Fort Huachuca 10DaxOu
3 NaN Mozilla/5.0 (Linux; U; Android 4.1.2; en-us; S... en-US US Houston TysVFU
4 NaN Opera/9.80 (Android; Opera Mini/7.5.33286/29.3... en None NaN 10IGW7m
5 NaN Mozilla/5.0 (compatible; MSIE 10.0; Windows NT... en-US US Mishawaka 13GrCeP
6 NaN Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) G... en-US,en;q=0.5 US Hammond YmtpnZ
7 NaN Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_3_5 li... en-us None NaN 13oM0hV
8 NaN Mozilla/5.0 (iPhone; CPU iPhone OS 6_1_3 like ... en-us AU Sydney 15r91
9 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... en-US,en;q=0.8 None NaN 109LtDc
10 NaN Mozilla/5.0 (iPhone; CPU iPhone OS 6_1_3 like ... en-us US Middletown 109ar5F
11 NaN Mozilla/5.0 (iPhone; CPU iPhone OS 6_1_3 like ... en-us US Germantown 107xZnW
回答by BrenBarn
It appears that pandas and Numpy treat Nonespecially when comparing for equality. In pandas, Noneis supposed to be like NaN, representing a missing value. To find rows where the value is not None (or nan), you could do data_df[data_df.c.notnull()](or data_df[~data_df.c.isnull()]).
None在比较相等性时，pandas 和 Numpy 似乎特别对待。在Pandas中，None应该像 NaN 一样，代表一个缺失值。要查找值不是 None（或nan）的行，您可以执行data_df[data_df.c.notnull()]（或data_df[~data_df.c.isnull()]）。
