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 times the same row appears in the dataframe.
Group Size Time
Short Small 2
Moderate Medium 1
Moderate Small 1
Tall Large 1
You can use groupby's size
import pandas as pd
# load the sample data
data = {'Group': ['Short', 'Short', 'Moderate', 'Moderate', 'Tall'], 'Size': ['Small', 'Small', 'Medium', 'Small', 'Large']}
df = pd.DataFrame(data)
Option 1:
dfg = df.groupby(by=["Group", "Size"]).size()
# which results in a pandas.core.series.Series
Group Size
Moderate Medium 1
Small 1
Short Small 2
Tall Large 1
dtype: int64
Option 2:
dfg = df.groupby(by=["Group", "Size"]).size().reset_index(name="Time")
# which results in a pandas.core.frame.DataFrame
Group Size Time
0 Moderate Medium 1
1 Moderate Small 1
2 Short Small 2
3 Tall Large 1
Option 3:
dfg = df.groupby(by=["Group", "Size"], as_index=False).size()
# which results in a pandas.core.frame.DataFrame
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
margins=True to get the marginal counts!
df.value_counts().Other posibbility is using .pivot_table() and aggfunc='size'
df_solution = df.pivot_table(index=['Group','Size'], aggfunc='size')
