How can I know the type of a pandas dataframe cell

Question

I have a dataframe, for example:

1
1.3
2,5
4
5

With the following code, I am trying to know what are the types of the different cells of my pandas dataframe:

for i in range (len(data.columns)) :
                print (" lenth of  columns : " + str(len(data.columns)) )
                for j in range (len(data[i])) :
                    data[i][j]=re.sub(r'(\d*)\.(\d*)',r'\1,\2',str(data[i][j]))
                    print(str(data[i][j]))

                    print(" est de type : "type(data[i][j]))
                    if str(data[i][j]).isdigit():
                        print(str(data[i][j]) + " contain a number  " )

The problem is when a cell of the dataframe contain a dot, pandas thinks it is a string. So I used regex, in order to change the dot into a comma.

But after that, the types of all my dataframe cells changed to string. My question is: How can I know if a cell of the dataframe is an int or a float? I already tried isinstance(x, int)

edit : How can I count the number of int and float, with the output of the df.apply(type) for example , I want to know how many cells of my column are int or float

My second question is, why when I have 2.5 , the dataframe give him the str type ?

    0       <class 'int'>
1       <class 'str'>
2     <class 'float'>
3     <class 'float'>
4       <class 'int'>
5       <class 'str'>
6       <class 'str'>

Thanks.

Answer 1

If you have a column with different types, e.g.

>>> df = pd.DataFrame(data = {"l": [1,"a", 10.43, [1,3,4]]})
>>> df
           l
0          1
1          a
2      10.43
4  [1, 3, 4]

Pandas will just state that this Series is of dtype object. However, you can get each entry type by simply applying type function

>>> df.l.apply(type)
0     <type 'int'>
1     <type 'str'>
2     <type 'float'>
4     <type 'list'>

However, if you have a dataset with very different data types, you probably should reconsider its design..