本文介绍了如何使用单个命令[Python-Pandas]获取所有列的数据类型?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我想查看存储在我的数据框中的所有列的数据类型,而无需对其进行遍历.怎么了?
I want to see the datatype of all columns stored in my dataframe without iterating over them. What is the way?
推荐答案
10分钟熊猫有一个很好的例子,用于 DataFrame.dtypes
:
10 min to pandas has nice example for DataFrame.dtypes
:
df2 = pd.DataFrame({
'A' : 1.,
'B' : pd.Timestamp('20130102'),
'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
'D' : np.array([3] * 4,dtype='int32'),
'E' : pd.Categorical(["test","train","test","train"]),
'F' : 'foo' })
print (df2)
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
print (df2.dtypes)
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
但是使用dtypes=object
有点复杂(通常显然是string
):
But with dtypes=object
it is a bit complicated (generally, obviously it is string
):
示例:
df = pd.DataFrame({'strings':['a','d','f'],
'dicts':[{'a':4}, {'c':8}, {'e':9}],
'lists':[[4,8],[7,8],[3]],
'tuples':[(4,8),(7,8),(3,)],
'sets':[set([1,8]), set([7,3]), set([0,1])] })
print (df)
dicts lists sets strings tuples
0 {'a': 4} [4, 8] {8, 1} a (4, 8)
1 {'c': 8} [7, 8] {3, 7} d (7, 8)
2 {'e': 9} [3] {0, 1} f (3,)
所有值都具有相同的 dtypes
:
All values have same dtypes
:
print (df.dtypes)
dicts object
lists object
sets object
strings object
tuples object
dtype: object
但是type
是不同的,如果需要循环检查:
But type
is different, if need check it by loop:
for col in df:
print (df[col].apply(type))
0 <class 'dict'>
1 <class 'dict'>
2 <class 'dict'>
Name: dicts, dtype: object
0 <class 'list'>
1 <class 'list'>
2 <class 'list'>
Name: lists, dtype: object
0 <class 'set'>
1 <class 'set'>
2 <class 'set'>
Name: sets, dtype: object
0 <class 'str'>
1 <class 'str'>
2 <class 'str'>
Name: strings, dtype: object
0 <class 'tuple'>
1 <class 'tuple'>
2 <class 'tuple'>
Name: tuples, dtype: object
或具有 iat
:
Or first value of columns with iat
:
print (type(df['strings'].iat[0]))
<class 'str'>
print (type(df['dicts'].iat[0]))
<class 'dict'>
print (type(df['lists'].iat[0]))
<class 'list'>
print (type(df['tuples'].iat[0]))
<class 'tuple'>
print (type(df['sets'].iat[0]))
<class 'set'>
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