I am trying to change the column TOPIxd data to String type with the code below.
If you check the data type in data.dtypes, it will remain the object type as it was originally.
I wonder why...
import pandas as pd
import numpy as np
df = pd.read_csv("forcasting topx.csv", header = 0)
# Delete the opening few lines
data = df.drop ([0,1,2,3])
#Modification of type
data['TOPIX d'].astype(np.str)
data.dtypes
The numpy and pandas treat strings differently. numpy has a fixed length of np.str in an array, but pandas has only a pointer to an object in DataFrame.
I don't know why you're trying to change the data to String type, but if it's an element that originally becomes a string, you don't have to worry about it because each element of DataFrame is of type str.
Also, if an element contains a number and you want to change it to a string, you can convert it with astype(str). If you look at dtypes, it remains the object type, but if you look at each element type, it is the str type.
data=pd.DataFrame([['a'], ['b'], [3]])
>>>data=pd.DataFrame([['a'],['b'],[3]])
>> type (data.iloc [2,0])
<class 'int'>
>>>data[0]=data[0].astype(str)
>> type (data.iloc [2,0])
<class'str'>
pandas.DataFrame.astype simply returns what you cast and is not a function that changes the original dataframe content.Here is a sample code for this:
>>ser=pd.Series ([1,2], dtype='int32')
>>ser.astype('int64')
0 1
1 2
dtype —int64
>>ser
0 1
1 2
dtype —int32
If you want to treat it as a string value, you can substitute the result of astype or specify the dtype from the beginning using the optional argument dtype in read_csv.
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