for a, b in itertools.combinations(number_of_notes_cols, 2):
weekly_meetings_difference = all_meetings_data[(all_meetings_data[a] != all_meetings_data[b]) == True]
The code above used to work: it would return all the rows of all the combinations of pairs of weekly_meetings_difference's columns where the column values (if this was true for any pair of columns). Now, returning weekly_meetings_difference gives me some, but not all, of the rows where the column values changed.
Edit with some code:
Before (when everything seemed to be working fine):
Number of Notes 03112016 Number of Notes 03192016 Number of Notes 03272016 Number of Notes 04042016
Meeting Name
X 12.0 NaN NaN NaN
Y 5.0 5.0 NaN NaN
Z 2.0 NaN NaN NaN
W NaN 6.0 713.0 740.0
After (now that I've updated the original dataframe from which I want information):
Number of Notes 03112016 Number of Notes 03192016 Number of Notes 03272016 Number of Notes 04042016 Number of Notes 04122016 Emails 04122016
Meeting Name
A 37.0 37.0 38.0 38.0 37.0
X 12.0 NaN NaN NaN NaN NaN
Y 5.0 5.0 NaN NaN NaN NaN
Z 2.0 NaN NaN NaN NaN NaN
Now that I've done this edit, I am noticing row A was added after adding the extra column to the dataframe as well as row W being removed (they both should show each time)
First, let me make sure that I understand the problem. Are you looking for rows in a dataframe that have more than one unique value? That is, the value changes at least one time in the row.
import pandas as pd
df = pd.DataFrame({'a': [1, 1, 1], 'b': [1, 2, 3], 'c': [1, 1, 3]})
a b c
0| 1 1 1
1| 1 2 1
2| 1 3 3
In the dataframe above, you would want rows 1 and 2. If so, I would do something like:
df.apply(pd.Series.nunique, axis=1)
Which returns the number of unique values in each row of the dataframe:
0 1
1 2
2 2
dtype: int64
Using that result, we can select the rows we care about with:
df[df.apply(pd.Series.nunique, axis=1) > 1]
Which returns the expected:
a b c
1| 1 2 1
2| 1 3 3
Is this what you're after, or is it something else? Happy to edit if you clarify.