Hello I have the following dataframe.
    Group           Size
    Short          Small
    Short          Small
    Moderate       Medium
    Moderate       Small
    Tall           Large
I want to count the frequency of how many time the same row appears in the dataframe.
    Group           Size      Time
    Short          Small        2
    Moderate       Medium       1 
    Moderate       Small        1
    Tall           Large        1
                Using the size() or count() method with pandas. DataFrame. groupby() will generate the count of a number of occurrences of data present in a particular column of the dataframe.
In pandas you can get the count of the frequency of a value that occurs in a DataFrame column by using Series. value_counts() method, alternatively, If you have a SQL background you can also get using groupby() and count() method.
After grouping a DataFrame object on one column, we can apply count() method on the resulting groupby object to get a DataFrame object containing frequency count. This method can be used to count frequencies of objects over single or multiple columns.
You can use groupby's size:
In [11]: df.groupby(["Group", "Size"]).size()
Out[11]:
Group     Size
Moderate  Medium    1
          Small     1
Short     Small     2
Tall      Large     1
dtype: int64
In [12]: df.groupby(["Group", "Size"]).size().reset_index(name="Time")
Out[12]:
      Group    Size  Time
0  Moderate  Medium     1
1  Moderate   Small     1
2     Short   Small     2
3      Tall   Large     1
                        Update after pandas 1.1 value_counts now accept multiple columns
df.value_counts(["Group", "Size"])
You can also try pd.crosstab()
Group           Size
Short          Small
Short          Small
Moderate       Medium
Moderate       Small
Tall           Large
pd.crosstab(df.Group,df.Size)
Size      Large  Medium  Small
Group                         
Moderate      0       1      1
Short         0       0      2
Tall          1       0      0
EDIT: In order to get your out put
pd.crosstab(df.Group,df.Size).replace(0,np.nan).\
     stack().reset_index().rename(columns={0:'Time'})
Out[591]: 
      Group    Size  Time
0  Moderate  Medium   1.0
1  Moderate   Small   1.0
2     Short   Small   2.0
3      Tall   Large   1.0
                        Other posibbility is using .pivot_table() and aggfunc='size'
df_solution = df.pivot_table(index=['Group','Size'], aggfunc='size')
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