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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