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How to convert datatype:object to float64 in python?

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to convert datatype:object to float64 in python in Python. So Here I am Explain to you all the possible Methods here.

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Table of Contents

How to convert datatype:object to float64 in python?

  1. How to convert datatype:object to float64 in python?

    After cleaning out the internal header rows from df, the columns' values were of “non-null object” type (DataFrame.info()).

  2. convert datatype:object to float64 in python

    After cleaning out the internal header rows from df, the columns' values were of “non-null object” type (DataFrame.info()).

Method 1

You can convert most of the columns by just calling convert_objects:

In [36]:

df = df.convert_objects(convert_numeric=True)
df.dtypes
Out[36]:
Date         object
WD            int64
Manpower    float64
2nd          object
CTR          object
2ndU        float64
T1            int64
T2          int64
T3           int64
T4        float64
dtype: object

For column ‘2nd’ and ‘CTR’ we can call the vectorised str methods to replace the thousands separator and remove the ‘%’ sign and then astype to convert:

In [39]:

df['2nd'] = df['2nd'].str.replace(',','').astype(int)
df['CTR'] = df['CTR'].str.replace('%','').astype(np.float64)
df.dtypes
Out[39]:
Date         object
WD            int64
Manpower    float64
2nd           int32
CTR         float64
2ndU        float64
T1            int64
T2            int64
T3            int64
T4           object
dtype: object
In [40]:

df.head()
Out[40]:
        Date  WD  Manpower   2nd   CTR  2ndU   T1    T2   T3     T4
0   2013/4/6   6       NaN  2645  5.27  0.29  407   533  454    368
1   2013/4/7   7       NaN  2118  5.89  0.31  257   659  583    369
2  2013/4/13   6       NaN  2470  5.38  0.29  354   531  473    383
3  2013/4/14   7       NaN  2033  6.77  0.37  396   748  681    458
4  2013/4/20   6       NaN  2690  5.38  0.29  361   528  541    381

Or you can do the string handling operations above without the call to astype and then call convert_objects to convert everything in one go.

Method 2

I had this problem in a DataFrame (df) created from an Excel-sheet with several internal header rows.

After cleaning out the internal header rows from df, the columns’ values were of “non-null object” type (DataFrame.info()).

This code converted all numerical values of multiple columns to int64 and float64 in one go:

for i in range(0, len(df.columns)):
    df.iloc[:,i] = pd.to_numeric(df.iloc[:,i], errors='ignore')
    # errors='ignore' lets strings remain as 'non-null objects'

Conclusion

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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