Python 通过 dtype 选择 Pandas 列
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Selecting Pandas Columns by dtype
提问by caner
I was wondering if there is an elegant and shorthand way in Pandas DataFrames to select columns by data type (dtype). i.e. Select only int64 columns from a DataFrame.
我想知道 Pandas DataFrames 中是否有一种优雅的速记方式来按数据类型（dtype）选择列。即仅从 DataFrame 中选择 int64 列。
To elaborate, something along the lines of
详细说明，类似于
df.select_columns(dtype=float64)
Thanks in advance for the help
在此先感谢您的帮助
采纳答案by Dan Allan
df.loc[:, df.dtypes == np.float64]
回答by normonics
df.select_dtypes(include=[np.float64])
回答by Andy Hayden
Since 0.14.1 there's a select_dtypesmethod so you can do this more elegantly/generally.
从 0.14.1 开始，有一种select_dtypes方法可以让您更优雅/更一般地执行此操作。
In [11]: df = pd.DataFrame([[1, 2.2, 'three']], columns=['A', 'B', 'C'])
In [12]: df.select_dtypes(include=['int'])
Out[12]:
A
0 1
To select all numeric types use the numpy dtype numpy.number
要选择所有数字类型，请使用 numpy dtype numpy.number
In [13]: df.select_dtypes(include=[np.number])
Out[13]:
A B
0 1 2.2
In [14]: df.select_dtypes(exclude=[object])
Out[14]:
A B
0 1 2.2
回答by MaxU
I'd like to extend existing answer by adding options for selecting all floatingdtypes or all integerdtypes:
我想通过添加用于选择所有浮动dtype 或所有整数dtype 的选项来扩展现有答案：
Demo:
演示：
np.random.seed(1234)
df = pd.DataFrame({
'a':np.random.rand(3),
'b':np.random.rand(3).astype('float32'),
'c':np.random.randint(10,size=(3)).astype('int16'),
'd':np.arange(3).astype('int32'),
'e':np.random.randint(10**7,size=(3)).astype('int64'),
'f':np.random.choice([True, False], 3),
'g':pd.date_range('2000-01-01', periods=3)
})
yields:
产量：
In [2]: df
Out[2]:
a b c d e f g
0 0.191519 0.785359 6 0 7578569 False 2000-01-01
1 0.622109 0.779976 8 1 7981439 True 2000-01-02
2 0.437728 0.272593 0 2 2558462 True 2000-01-03
In [3]: df.dtypes
Out[3]:
a float64
b float32
c int16
d int32
e int64
f bool
g datetime64[ns]
dtype: object
Selecting all floating numbercolumns:
选择所有浮点数列：
In [4]: df.select_dtypes(include=['floating'])
Out[4]:
a b
0 0.191519 0.785359
1 0.622109 0.779976
2 0.437728 0.272593
In [5]: df.select_dtypes(include=['floating']).dtypes
Out[5]:
a float64
b float32
dtype: object
Selecting all integer numbercolumns:
选择所有整数列：
In [6]: df.select_dtypes(include=['integer'])
Out[6]:
c d e
0 6 0 7578569
1 8 1 7981439
2 0 2 2558462
In [7]: df.select_dtypes(include=['integer']).dtypes
Out[7]:
c int16
d int32
e int64
dtype: object
Selecting all numericcolumns:
选择所有数字列：
In [8]: df.select_dtypes(include=['number'])
Out[8]:
a b c d e
0 0.191519 0.785359 6 0 7578569
1 0.622109 0.779976 8 1 7981439
2 0.437728 0.272593 0 2 2558462
In [9]: df.select_dtypes(include=['number']).dtypes
Out[9]:
a float64
b float32
c int16
d int32
e int64
dtype: object
回答by hui chen
Optionally if you don't want to create a subset of the dataframe during the process, you can directly iterate through the column datatype.
或者，如果您不想在此过程中创建数据帧的子集，您可以直接遍历列数据类型。
I haven't benchmarked the code below, assume it will be faster if you work on very large dataset.
我还没有对下面的代码进行基准测试，假设你处理非常大的数据集会更快。
[col for col in df.columns.tolist() if df[col].dtype not in ['object','<M8[ns]']]
回答by Gurubux
Multiple includes for selecting columns with list of types for example- float64 and int64
多个包含用于选择具有类型列表的列，例如 float64 和 int64
df_numeric = df.select_dtypes(include=[np.float64,np.int64])
回答by Anjan Prasad
select_dtypes(include=[np.int])
select_dtypes(include=[np.int])
回答by Jake Drew
If you want to select int64 columns and then update "in place", you can use:
如果要选择 int64 列然后“就地”更新，可以使用：
int64_cols = [col for col in df.columns if is_int64_dtype(df[col].dtype)]
df[int64_cols]
For example, notice that I update all the int64 columns in df to zero below:
例如，请注意我将下面的 df 中的所有 int64 列更新为零：
In [1]:
import pandas as pd
from pandas.api.types import is_int64_dtype
df = pd.DataFrame({'a': [1, 2] * 3,
'b': [True, False] * 3,
'c': [1.0, 2.0] * 3,
'd': ['red','blue'] * 3,
'e': pd.Series(['red','blue'] * 3, dtype="category"),
'f': pd.Series([1, 2] * 3, dtype="int64")})
int64_cols = [col for col in df.columns if is_int64_dtype(df[col].dtype)]
print('int64 Cols: ',int64_cols)
print(df[int64_cols])
df[int64_cols] = 0
print(df[int64_cols])
Out [1]:
int64 Cols: ['a', 'f']
a f
0 1 1
1 2 2
2 1 1
3 2 2
4 1 1
5 2 2
a f
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
Just for completeness:
只是为了完整性：
df.loc() and df.select_dtypes() are going to give a copy of a slice from the dataframe. This means that if you try to update values from df.select_dtypes(), you will get a SettingWithCopyWarning and no updates will happen to df in place.
df.loc() 和 df.select_dtypes() 将提供数据帧中切片的副本。这意味着如果您尝试从 df.select_dtypes() 更新值，您将获得 SettingWithCopyWarning 并且不会对 df 进行更新。
For example, notice when I try to update df using .loc() or .select_dtypes() to select columns, nothing happens:
例如，请注意，当我尝试使用 .loc() 或 .select_dtypes() 更新 df 以选择列时，没有任何反应：
In [2]:
df = pd.DataFrame({'a': [1, 2] * 3,
'b': [True, False] * 3,
'c': [1.0, 2.0] * 3,
'd': ['red','blue'] * 3,
'e': pd.Series(['red','blue'] * 3, dtype="category"),
'f': pd.Series([1, 2] * 3, dtype="int64")})
df_bool = df.select_dtypes(include='bool')
df_bool.b[0] = False
print(df_bool.b[0])
print(df.b[0])
df.loc[:, df.dtypes == np.int64].a[0]=7
print(df.a[0])
Out [2]:
False
True
1
