In general, this error occurs if you try to access an attribute that doesn't exist on an object. For pandas Serieses (or DataFrames), it occurs because you tried to index it using the attribute access (.).

In the case in the OP, they used x.colNames[0] to access the value on colNames[0] in row x but df doesn't have attribute colNames, so the error occurred.¹

Another case this error may occur is if an index had a white space in it that you didn't know about. For example, the following case reproduces this error.

s = pd.Series([1, 2], index=[' a', 'b'])
s.a

In this case, make sure to remove the white space:

s.index = [x.strip() for x in s.index]
# or
s.index = [x.replace(' ', '') for x in s.index]

Finally, it's always safe to use [] to index a Series (or a DataFrame).

^{1: Serieses have the following attributes: axes, dtypes, empty, index, ndim, size, shape, T, values. DataFrames have all of these attributes + columns. When you use df.apply(..., axis=1), it iterates over the rows where each row is a Series whose indices are the column names of df.}

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html

data : array-like, dict, or scalar value Contains data stored in Series

Don't think a set counts as array-like, so just convert it to a list before calling pd.Series.

import pandas as pd
numbers = {1,2,3,4,5}
ser = pd.Series(list(numbers))

Output:

print ser

0    1
1    2
2    3
3    4
4    5

data.hist(column ='msg_length', by='label')  

did the job

@Serge Ballesta's comment is the most likely cause.

There are typos in the code that you have shared. Check whether you called value instead of values.

The following code works as expected:

import pandas as pd

data = {'phone': [25470000000, 25470000000, 25470000010, 25470000020, 25470000000], 'group': ['MAMA', 'MAMA', 'MAMA', 'MAMA', 'MAMA'], 'County': ['Orange', 'Orange', 'Orange', 'Orange', 'Orange'], 'PNC/ANC': ['PNC', 'PNC', 'PNC', 'PNC', 'PNC'], 'Facility Name': ['Main Centre', 'Main Centre', 'Centre', 'Centre', 'Main Centre'], 'Optedout': ['FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'Facility Code': [112, 112, 108, 108, 112]}

merged_df = pd.DataFrame.from_dict(data)

facility_number = 108
group = 'MAMA'
optout = 'FALSE'

phone_numbers = merged_df.loc[(merged_df['Facility Code'] ==facility_number) & (merged_df['group'] == group) & (merged_df['Optedout'] == optout)]['phone'].values

print(phone_numbers)

Output:

[25470000010 25470000020]

By removing .values, the output is a dataframe:

2    25470000010
3    25470000020
Name: phone, dtype: int64

While subtracting the dates you should use the following code.

df = pd.DataFrame([ pd.Timestamp('20010101'), pd.Timestamp('20040605') ])
(df.loc[0]-df.loc[1]).astype('timedelta64[D]')

So basically use .astype('timedelta64[D]') on the subtracted column.