How to get datatypes of all columns using a single command [ Python - Pandas ]?

Question

I wanted to know the data type of each column which is stored in my data frame. How do I code that?

Answer 1

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

But with dtypes=object it is a bit complicated:

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,)

If you see all data types are the same:

print (df.dtypes)

dicts object

lists object

sets object

strings object

tuples object

dtype: object

But the type is different, you can check it in the below 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

The first value in the 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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