As a newbie to pandas, I'm looking to get a count of values from a specific column and percent count into a single frame. I can get one or the other, but can't figure out how to add or merge them into a single frame. Thoughts?
The frame/table should be like this:
some_value, count, count(as %)
Here is what I have...
import numpy as np
import pandas as pd
np.random.seed(1)
values = np.random.randint(30, 35, 20)
df1 = pd.DataFrame(values, columns=['some_value'])
df1.sort_values(by=['some_value'], inplace = True)
df2 = df1.value_counts()
df3 = df1.value_counts(normalize=True)
print(df2)
print("------")
print(df3)
Just use
pd.DataFrame({"count":df2,"%":df3*100})
to put the series into one df.
Output:
count %
some_value
34 7 35.0
32 4 20.0
33 3 15.0
31 3 15.0
30 3 15.0
I guess calling value_counts
and then normalizing it with a lambda function could be more efficient, but you can get the result you are seeking by doing :
df1_counts = df1.value_counts().to_frame(name="count").merge(
df1.value_counts(normalize=True).to_frame(name="count(as %)"),
left_index=True,
right_index=True,
)
Resulting in :
| some_value | count | count(as %) |
|------------|-------|-------------|
| 34 | 7 | 0.35 |
| 32 | 4 | 0.20 |
| 33 | 3 | 0.15 |
| 31 | 3 | 0.15 |
| 30 | 3 | 0.15 |
Best !
Compute, rename and join. Lets try;
df1.some_value.value_counts().to_frame('count').join(df1.some_value.value_counts(normalize=True).to_frame('%'))
count %
34 7 0.35
32 4 0.20
33 3 0.15
31 3 0.15
30 3 0.15
Try this using partial
from functools
with pd.DataFrame.agg
calling a list of functions:
from functools import partial
vc_norm = partial(pd.Series.value_counts, normalize=True)
df1['some_value'].agg([pd.Series.value_counts, vc_norm])
Output:
value_counts value_counts
34 7 0.35
32 4 0.20
31 3 0.15
30 3 0.15
33 3 0.15
Or you can use lambda function like this:
df1['some_value'].agg([pd.Series.value_counts, lambda x: x.value_counts(normalize=True)])
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