ValueError: Grouper for not 1-dimensio

Question

I'm have the following code which creates a table and a barplot via seaborn.

#Building a dataframe grouped by the # of Engagement Types
sales_type = sales.groupby('# of Engagement Types').sum()

#Calculating the % of people who bought the course by # engagement types
sales_type['% Sales per Participants'] =  round(100*(sales_type['Sales'] / sales_type['Had an Engagement']), 2)

#Calculating the # of people who didn't have any engagements
sales_type.set_value(index=0, col='Had an Engagement', value=sales[sales['Had an Engagement']==0].count()['Sales'])

#Calculating the % of sales for those who didn't have any engagements
sales_type.set_value(index=0, col='% Sales per Participants',
                     value=round(100 * (sales_type.ix[0, 'Sales'] / 
                                        sales[sales['Had an Engagement']==0].count()['Sales']),2))

#Setting the graph image
fig, (ax1) = plt.subplots(nrows=1, ncols=1, figsize=(12,4))
sns.set_style("whitegrid")

# Ploting the histagram for the % of total prospects
ax1 = sns.barplot(x=sales_type.index,y='% Sales per Participants', data=sales_type ,ax=ax1)
ax1.set(ylabel = '%')
ax1.set_title('% Sales per Participants By # of Engagement Types') 

#present the table
sales_type.xs(['Had an Engagement', 'Sales','% Sales per Participants'],axis=1).transpose()
#sales_type

I'm using the same code concept for other parameters I have with no issue. However, for one parameter I get an error: "ValueError: Grouper for '' not 1-dimensional" for the line code:

ax1 = sns.barplot(x=sales_type.index,y='% Sales per Participants', data=sales_type ,ax=ax1)

This error occurs although the dataframe doesn't have more than one dimension.

This is the head of the table:

                       Sales  Pre-Ordered / Ordered Book  \
# of Engagement Types                                      
0                        1.0                         0.0   
1                       20.0                       496.0   
2                       51.0                       434.0   
3                       82.0                       248.0   
4                       71.0                       153.0   
5                       49.0                        97.0   
6                        5.0                        24.0   

                       Opted In For / Clicked to Kindle  Viewed PLC  \
# of Engagement Types                                                 
0                                                   0.0           0   
1                                               27034.0        5920   
2                                                6953.0        6022   
3                                                1990.0        1958   
4                                                 714.0         746   
5                                                 196.0         204   
6                                                  24.0          24   

                       # of PLC Engagement  Viewed Webinar  \
# of Engagement Types                                        
0                                      0.0               0   
1                                   6434.0            1484   
2                                   7469.0            1521   
3                                   2940.0            1450   
4                                   1381.0             724   
5                                    463.0             198   
6                                     54.0              24   

                       # of Webinars (Live/Replay)  \
# of Engagement Types                                
0                                              0.0   
1                                           1613.0   
2                                           1730.0   
3                                           1768.0   
4                                           1018.0   
5                                            355.0   
6                                             45.0   

                       OCCC Facebook Group Member  Engaged in Cart-Open  \
# of Engagement Types                                                     
0                                             0.0                     0   
1                                           148.0                   160   
2                                           498.0                  1206   
3                                           443.0                   967   
4                                           356.0                   511   
5                                           168.0                   177   
6                                            24.0                    24   

                       # of Engagement at Cart Open  Had an Engagement  \
# of Engagement Types                                                    
0                                               0.0               3387   
1                                             189.0              35242   
2                                            1398.0               8317   
3                                            1192.0               2352   
4                                             735.0                801   
5                                             269.0                208   
6                                              40.0                 24   

                       Total # of Engagements  % Sales per Participants  
# of Engagement Types                                                    
0                                         0.0                      0.03  
1                                     35914.0                      0.06  
2                                     18482.0                      0.61  
3                                      8581.0                      3.49  
4                                      4357.0                      8.86  
5                                      1548.0                     23.56  
6                                       211.0                     20.83  

This is the full error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-211-f0185fe64c1a> in <module>()
     12 sns.set_style("whitegrid")
     13 # Ploting the histagram for the % of total prospects
---> 14 ax1 = sns.barplot(x=sales_type.index,y='% Sales per Participants', data=sales_type ,ax=ax1)
     15 ax1.set(ylabel = '%')
     16 ax1.set_title('% Sales per Participants By # of Engagement Types')

ValueError: Grouper for '<class 'pandas.core.frame.DataFrame'>' not 1-dimensional

I've tried to search the internet and Stack Overflow for this error, but got no results. Does anyone has an idea what's going on?

Answer 1

Happened to me when I accidentally created MultiIndex columns:

>>> values = np.asarray([[1, 1], [2, 2], [3, 3]])

# notice accidental double brackets around column list
>>> df = pd.DataFrame(values, columns=[["foo", "bar"]])

# prints very innocently
>>> df
  foo bar
0   1   1
1   2   2
2   3   3

# but throws this error
>>> df.groupby("foo")
ValueError: Grouper for 'foo' not 1-dimensional

# cause:
>>> df.columns
MultiIndex(levels=[['bar', 'foo']],
           labels=[[1, 0]])

# fix by using correct columns list
>>> df = pd.DataFrame(values, columns=["foo", "bar"])
>>> df.groupby("foo")
<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7f9a280cbb70>

Answer 2

Something to add to @w-m's answer.

If you are adding multiple columns from one dataframe to another:

df1[['col1', 'col2']] = df2[['col1', 'col2']]

it will create a multi-column index and if you try to group by anything on df1, it will give you this error.

To solve this, get rid of the multi-index by using

df1.columns = df1.columns.get_level_values(0)

Answer 3

Simplified problem

I also ran into this problem, and found the cause of it and the obvious solution

To recreate this:

df = pd.DataFrame({"foo": [1,2,3], "bar": [1,2,3]})
df.rename(columns={'foo': 'bar'}, inplace=True)

   bar  bar
0    1    1
1    2    2
2    3    3

df.groupby('bar')

ValueError: Grouper for 'bar' not 1-dimensional

Just like a lot of cryptic pandas errors, this one too stems from having two columns with the same name.

Figure out which one you want to use, rename or drop the other column and redo the operation.

Solution

Rename the columns like this

df.columns = ['foo', 'bar']

   foo  bar
0    1    1
1    2    2
2    3    3

df.groupby('bar')
<pandas.core.groupby.DataFrameGroupBy object at 0x1066dd950>