将一列附加到 Pandas 中的数据框

Question

I am trying to append a numpy.darray to a dataframe with little success. The dataframe is called user2 and the numpy.darray is called CallTime.

I tried:

user2["CallTime"] = CallTime.values

but I get an error message:

Traceback (most recent call last):
File "<ipython-input-53-fa327550a3e0>", line 1, in <module>
user2["CallTime"] = CallTime.values
AttributeError: 'numpy.ndarray' object has no attribute 'values'

Then I tried:

user2["CallTime"] = user2.assign(CallTime = CallTime.values)

but I get again the same error message as above.

I also tried to use the merge command but for some reason it was not recognized by Python although I have imported pandas. In the example below CallTime is a dataframe:

 user3 = merge(user2, CallTime)

Error message:

  Traceback (most recent call last):
  File "<ipython-input-56-0ebf65759df3>", line 1, in <module>
  user3 = merge(user2, CallTime)
  NameError: name 'merge' is not defined

Any ideas?

Thank you!

Answer 1

pandas DataFrame is a 2-dimensional data structure, and each column of a DataFrame is a 1-dimensional Series . So if you want to add one column to a DataFrame, you must first convert it into Series . np.ndarray is a multi-dimensional data structure. From your code, I believe the shape of np.ndarray CallTime should be nx1 ( n rows and 1 colmun), and it's easy to convert it to a Series. Here is an example:

df = DataFrame(np.random.rand(5,2), columns=['A', 'B'])

This creates a dataframe df with two columns 'A' and 'B', and 5 rows.

CallTime = np.random.rand(5,1)

Assume this is your np.ndarray data CallTime

df['C'] = pd.Series(CallTime[:, 0])

This will add a new column to df . Here CallTime[:,0] is used to select first column of CallTime , so if you want to use different column from np.ndarray , change the index.

Please make sure that the number of rows for df and CallTime are equal.

Hope this would be helpful.

Answer 2

I think instead to provide only documentation, I will try to provide a sample:

import numpy as np
import pandas as pd

data = {'A': [2010, 2011, 2012],
    'B': ['Bears', 'Bears', 'Bears'],
    'C': [11, 8, 10],
    'D': [5, 8, 6]}
user2 = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])

#creating the array what will append to pandas dataframe user2
CallTime = np.array([1, 2, 3])

#convert to list the ndarray array CallTime, if you your CallTime is a matrix than after converting to list you can iterate or you can convert into dataframe and just append column required or just join the dataframe.

user2.loc[:,'CallTime'] = CallTime.tolist()


print(user2)

I think this one will help, also check numpy.ndarray.tolist documentation if need to find out why we need the list and how to do, also here is example how to create dataframe from numpy in case of need https://stackoverflow.com/a/35245297/2027457

Answer 3

Here is a simple solution.

user2["CallTime"] = CallTime

The problem here for you is that CallTime is an array, you couldn't use .values. Since .values is used to convert a dataframe to array. For example,

df = DataFrame(np.random.rand(10,2), columns=['A', 'B'])
# The followings are correct
df.values
df['A'].values
df['B'].values