Python 根据数据类型获取熊猫数据框列的列表
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get list of pandas dataframe columns based on data type
提问by yoshiserry
If I have a dataframe with the following columns:
如果我有一个包含以下列的数据框：
1. NAME object
2. On_Time object
3. On_Budget object
4. %actual_hr float64
5. Baseline Start Date datetime64[ns]
6. Forecast Start Date datetime64[ns]
I would like to be able to say: here is a dataframe, give me a list of the columns which are of type Object or of type DateTime?
我想说的是：这是一个数据框，给我一个 Object 类型或 DateTime 类型的列的列表？
I have a function which converts numbers (Float64) to two decimal places, and I would like to use this list of dataframe columns, of a particular type, and run it through this function to convert them all to 2dp.
我有一个将数字 (Float64) 转换为两位小数的函数，我想使用此特定类型的数据帧列列表，并通过此函数运行它以将它们全部转换为 2dp。
Maybe:
也许：
For c in col_list: if c.dtype = "Something"
list[]
List.append(c)?
采纳答案by DSM
If you want a list of columns of a certain type, you can use groupby:
如果您想要某种类型的列列表，您可以使用groupby：
>>> df = pd.DataFrame([[1, 2.3456, 'c', 'd', 78]], columns=list("ABCDE"))
>>> df
A B C D E
0 1 2.3456 c d 78
[1 rows x 5 columns]
>>> df.dtypes
A int64
B float64
C object
D object
E int64
dtype: object
>>> g = df.columns.to_series().groupby(df.dtypes).groups
>>> g
{dtype('int64'): ['A', 'E'], dtype('float64'): ['B'], dtype('O'): ['C', 'D']}
>>> {k.name: v for k, v in g.items()}
{'object': ['C', 'D'], 'int64': ['A', 'E'], 'float64': ['B']}
回答by Andy Hayden
You can use boolean mask on the dtypes attribute:
您可以在 dtypes 属性上使用布尔掩码：
In [11]: df = pd.DataFrame([[1, 2.3456, 'c']])
In [12]: df.dtypes
Out[12]:
0 int64
1 float64
2 object
dtype: object
In [13]: msk = df.dtypes == np.float64 # or object, etc.
In [14]: msk
Out[14]:
0 False
1 True
2 False
dtype: bool
You can look at just those columns with the desired dtype:
您可以仅查看具有所需 dtype 的那些列：
In [15]: df.loc[:, msk]
Out[15]:
1
0 2.3456
Now you can use round (or whatever) and assign it back:
现在您可以使用 round（或其他）并将其分配回：
In [16]: np.round(df.loc[:, msk], 2)
Out[16]:
1
0 2.35
In [17]: df.loc[:, msk] = np.round(df.loc[:, msk], 2)
In [18]: df
Out[18]:
0 1 2
0 1 2.35 c
回答by qmorgan
As of pandas v0.14.1, you can utilize select_dtypes()to select columns by dtype
从 pandas v0.14.1 开始，您可以利用 dtypeselect_dtypes()选择列
In [2]: df = pd.DataFrame({'NAME': list('abcdef'),
'On_Time': [True, False] * 3,
'On_Budget': [False, True] * 3})
In [3]: df.select_dtypes(include=['bool'])
Out[3]:
On_Budget On_Time
0 False True
1 True False
2 False True
3 True False
4 False True
5 True False
In [4]: mylist = list(df.select_dtypes(include=['bool']).columns)
In [5]: mylist
Out[5]: ['On_Budget', 'On_Time']
回答by qmorgan
If you want a list of only the object columns you could do:
如果您只需要对象列的列表，您可以执行以下操作：
non_numerics = [x for x in df.columns \
if not (df[x].dtype == np.float64 \
or df[x].dtype == np.int64)]
and then if you want to get another list of only the numerics:
然后如果你想得到另一个只有数字的列表：
numerics = [x for x in df.columns if x not in non_numerics]
回答by Ashish Sahu
回答by Tanmoy
list(df.select_dtypes(['object']).columns)
This should do the trick
这应该可以解决问题
回答by Koo
use df.info(verbose=True)where dfis a pandas datafarme, by default verbose=False
默认情况下使用df.info(verbose=True)哪里df是熊猫数据场verbose=False
回答by MLKing
The most direct way to get a list of columns of certain dtype e.g. 'object':
获取某些 dtype 的列列表的最直接方法，例如“对象”：
df.select_dtypes(include='object').columns
For example:
例如：
>>df = pd.DataFrame([[1, 2.3456, 'c', 'd', 78]], columns=list("ABCDE"))
>>df.dtypes
A int64
B float64
C object
D object
E int64
dtype: object
To get all 'object' dtype columns:
要获取所有“对象”dtype 列：
>>df.select_dtypes(include='object').columns
Index(['C', 'D'], dtype='object')
For just the list:
仅用于列表：
>>list(df.select_dtypes(include='object').columns)
['C', 'D']
回答by geekidharsh
I came up with this three liner.
我想出了这三个班轮。
Essentially, here's what it does:
本质上，这是它的作用：
- Fetch the column names and their respective data types.
- I am optionally outputting it to a csv.
- 获取列名及其各自的数据类型。
- 我可以选择将其输出到 csv。
inp = pd.read_csv('filename.csv') # read input. Add read_csv arguments as needed
columns = pd.DataFrame({'column_names': inp.columns, 'datatypes': inp.dtypes})
columns.to_csv(inp+'columns_list.csv', encoding='utf-8') # encoding is optional
This made my life much easier in trying to generate schemason the fly. Hope this helps
这让我在尝试动态生成模式时变得更加轻松。希望这可以帮助
回答by itthrill
for yoshiserry;
为yoshiserry;
def col_types(x,pd):
dtypes=x.dtypes
dtypes_col=dtypes.index
dtypes_type=dtypes.value
column_types=dict(zip(dtypes_col,dtypes_type))
return column_types
