Python 大熊猫使用startswith从Dataframe中选择
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pandas select from Dataframe using startswith
提问by dartdog
This works (using Pandas 12 dev)
这有效（使用 Pandas 12 dev）
table2=table[table['SUBDIVISION'] =='INVERNESS']
Then I realized I needed to select the field using "starts with" Since I was missing a bunch. So per the Pandas doc as near as I could follow I tried
然后我意识到我需要使用“开始于”来选择字段，因为我错过了一堆。所以根据 Pandas doc 尽可能接近我尝试过的
criteria = table['SUBDIVISION'].map(lambda x: x.startswith('INVERNESS'))
table2 = table[criteria]
And got AttributeError: 'float' object has no attribute 'startswith'
并得到 AttributeError: 'float' object has no attribute 'startswith'
So I tried an alternate syntax with the same result
所以我尝试了一种具有相同结果的替代语法
table[[x.startswith('INVERNESS') for x in table['SUBDIVISION']]]
Reference http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexingSection 4: List comprehensions and map method of Series can also be used to produce more complex criteria:
参考http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing第 4 节：Series 的列表推导式和映射方法也可用于生成更复杂的标准：
What am I missing?
我错过了什么？
采纳答案by Andy Hayden
You can use the str.startswithDataFrame method to give more consistent results:
您可以使用str.startswithDataFrame 方法提供更一致的结果：
In [11]: s = pd.Series(['a', 'ab', 'c', 11, np.nan])
In [12]: s
Out[12]:
0 a
1 ab
2 c
3 11
4 NaN
dtype: object
In [13]: s.str.startswith('a', na=False)
Out[13]:
0 True
1 True
2 False
3 False
4 False
dtype: bool
and the boolean indexing will work just fine (I prefer to use loc, but it works just the same without):
并且布尔索引将工作得很好（我更喜欢使用loc，但没有它的工作原理相同）：
In [14]: s.loc[s.str.startswith('a', na=False)]
Out[14]:
0 a
1 ab
dtype: object
.
.
It looks least one of your elements in the Series/column is a float, which doesn't have a startswith method hence the AttributeError, the list comprehension should raise the same error...
它看起来系列/列中至少有一个元素是浮点数，它没有startswith方法因此AttributeError，列表理解应该引发相同的错误......
回答by Vinoj John Hosan
To retrieve all the rows which startwithrequired string
检索以所需字符串开头的所有行
dataFrameOut = dataFrame[dataFrame['column name'].str.match('string')]
To retrieve all the rows which containsrequired string
检索包含所需字符串的所有行
dataFrameOut = dataFrame[dataFrame['column name'].str.contains('string')]
回答by AleAve81
You can use applyto easily apply any string matching function to your column elementwise.
您可以apply轻松地将任何字符串匹配函数应用于您的列元素。
table2=table[table['SUBDIVISION'].apply(lambda x: x.startswith('INVERNESS'))]
this assuming that your "SUBDIVISION" column is of the correct type (string)
这假设您的“SUBDIVISION”列是正确的类型（字符串）
Edit: fixed missing parenthesis
编辑：修复缺少的括号
回答by Saurabh
Using startswith for a particular column value
对特定列值使用开始
df = df.loc[df["SUBDIVISION"].str.startswith('INVERNESS', na=False)]
