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Learn more about Teams readfile = pd.read_csv('6.csv') filevalues= readfile.loc[readfile['Customer'].str.contains(Department, na=False), 'June-18\nQty'] filevalues = filevalues.fillna(int(0)) int_series = filevalues.values.astype(int) calculated_series = int_series.apply(lambda x: filevalues*1.3) print(filevalues)

I am getting this error : AttributeError: 'numpy.ndarray' object has no attribute 'apply'

I have looked through this website and no solutions seems to work. I simply want to multiply the data by 1.3 in this series. Thank you

@RafaelC I was attempting to multiply every value in my list by 1.3. I used this method because supposedly it is supposed to convert the series into an int. – Michael Norman Jul 17, 2018 at 18:27 The reason is simple: there is no apply function in numpy arrays. There are, though, in pandas.Series objects, which you would have if you did filevalues.astype(int) instead of filevalues.values.astype(int) – rafaelc Jul 17, 2018 at 18:32

By taking .values you actually access the underlying numpy array; you no longer have a pandas.Series . numpy arrays do not have an apply method.

You are trying to use apply for a simple multiplication, which will be orders of magnitude slower than using a vectorized approach.

See below:

import pandas as pd
import numpy as np
df = pd.DataFrame({'a': np.arange(1000, dtype=np.float64)})
print(type(df['a']))
# Gives pandas.core.series.Series
print(type(df['a'].values))
# Gives numpy.ndarray
# The vectorized approach
df['a'] = df['a'] * 1.3
        
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