Develop Reference

pandas average across dynamic number of columns

I have a dataframe like as shown below
customer_id revenue_m7 revenue_m8 revenue_m9 revenue_m10
1 1234 1231 1256 1239
2 5678 3425 3255 2345
I would like to do the below
a) get average of revenue for each customer based on latest two columns (revenue_m9 and revenue_m10)
b) get average of revenue for each customer based on latest four columns (revenue_m7, revenue_m8, revenue_m9 and revenue_m10)
So, I tried the below
df['revenue_mean_2m'] = (df['revenue_m10']+df['revenue_m9'])/2
df['revenue_mean_4m'] = (df['revenue_m10']+df['revenue_m9']+df['revenue_m8']+df['revenue_m7'])/4
df['revenue_mean_4m'] = df.mean(axis=1) # i also tried this but how to do for only two columns (and not all columns)
But if I wish to compute average for past 12 months, then it may not be elegant to write this way. Is there any other better or efficient way to write this? I can just key in number of columns to look back and it can compute the average based on keyed in input
I expect my output to be like as below
customer_id revenue_m7 revenue_m8 revenue_m9 revenue_m10 revenue_mean_2m revenue_mean_4m
1 1234 1231 1256 1239 1867 1240
2 5678 3425 3255 2345 2800 3675.75

Use filter and slicing:
# keep only the "revenue_" columns
df2 = df.filter(like='revenue_')
# or
# df2 = df.filter(regex=r'revenue_m\d+')
# get last 2/4 columns and aggregate as mean
df['revenue_mean_2m'] = df2.iloc[:, -2:].mean(axis=1)
df['revenue_mean_4m'] = df2.iloc[:, -4:].mean(axis=1)
Output:
customer_id revenue_m7 revenue_m8 revenue_m9 revenue_m10 \
0 1 1234 1231 1256 1239
1 2 5678 3425 3255 2345
revenue_mean_2m revenue_mean_4m
0 1247.5 1240.00
1 2800.0 3675.75
if column order it not guaranteed
Sort them with natural sorting
# shuffle the DataFrame columns for demo
df = df.sample(frac=1, axis=1)
# filter and reorder the needed columns
from natsort import natsort_key
df2 = df.filter(regex=r'revenue_m\d+').sort_index(key=natsort_key, axis=1)

you could try something like this in reference to this post:
n_months = 4 # you could also do this in a loop for all months range(1, 12)
df[f'revenue_mean_{n_months}m'] = df.iloc[:, -n_months:-1].mean(axis=1)

count values of groups by consecutive days

i have data with 3 columns: date, id, sales.
my first task is filtering sales above 100. i did it.
second task, grouping id by consecutive days.
index
date
id
sales
0
01/01/2018
03
101
1
01/01/2018
07
178
2
02/01/2018
03
120
3
03/01/2018
03
150
4
05/01/2018
07
205
the result should be:
index
id
count
0
03
3
1
07
1
2
07
1
i need to do this task without using pandas/dataframe, but right now i can't imagine from which side attack this problem.
just for effort, i tried the suggestion for a solution here count consecutive days python dataframe
but the ids' not grouped.
here is my code:
data = df[df['sales'] >= 100]
data['date'] = pd.to_datetime(data['date']).dt.date
s = data.groupby('id').date.diff().dt.days.ne(1).cumsum()
new_frame = data.groupby(['id', s]).size().reset_index(level=0, drop=True)
it is very importent that the "new_frame" will have "count" column, because after i need to count id by range of those count days in "count" column. e.g. count of id's in range of 0-7 days, 7-12 days etc. but it's not part of my question.
Thank you a lot

Your code is close, but need some fine-tuning, as follows:
data = df[df['sales'] >= 100]
data['date'] = pd.to_datetime(data['date'], dayfirst=True)
df2 = data.sort_values(['id', 'date'])
s = df2.groupby('id').date.diff().dt.days.ne(1).cumsum()
new_frame = df2.groupby(['id', s]).size().reset_index(level=1, drop=True).reset_index(name='count')
Result:
print(new_frame)
id count
0 3 3
1 7 1
2 7 1
Summary of changes:
As your dates are in dd/mm/yyyy instead of the default mm/dd/yyyy, you have to specify the parameter dayfirst=True in pd.to_datetime(). Otherwise, 02/01/2018 will be regarded as 2018-02-01 instead of 2018-01-02 as expected and the day diff with adjacent entries will be around 30 as opposed to 1.
We added a sort step to sort by columns id and date to simplify the later grouping during the creation of the series s.
In the last groupby() the code reset_index(level=0, drop=True) should be dropping level=1 instead. Since, level=0 is the id fields which we want to keep.
In the last groupby(), we do an extra .reset_index(name='count') to make the Pandas series change back to a dataframe and also name the new column as count.

DataFrame Pivot Table run specific function on specific columns

I have an dataset that includes the below information. I'd like to write a pivot table that counts the number of days from the Date Column, and then runs sum on the Impression, Clicks, Conversions, and Budget Delivered columns. Essentially, I'd like a summary of the table
Date Impressions Clicks Conversions Budget Delivered
0 1/1/2019 11,506,995 1,672 88 $12,124.14
1 1/2/2019 9,394,458 1,516 179 $9,838.45
2 1/3/2019 4,696,388 878 129 $6,858.67
3 1/4/2019 8,987,784 1,179 107 $9,566.55
4 1/5/2019 8,923,751 1,171 88 $9,322
I am having trouble figuring out how to return this single row DataFrame. I am trying to use pivot_table method but the groupby parameter is not returning the desired result. Not sure how to approach this issue.

from datatable import dt, f, by
df = dt.Frame("""
Date Impressions Clicks Conversions Budget Delivered
1/1/2019 11,506,995 1,672 88 $12,124.14
1/2/2019 9,394,458 1,516 179 $9,838.45
1/3/2019 4,696,388 878 129 $6,858.67
1/4/2019 8,987,784 1,179 107 $9,566.55
1/5/2019 8,923,751 1,171 88 $9,322
""")
budget = df['Budget'].to_list()[0]
budget = [float(x.replace('$', '').replace(',', '')) for x in budget]
df['Budget'] = dt.Frame(budget)
df[:, dt.sum(f[1:6])]
| Impressions Clicks Conversions Budget Delivered
-- + ----------- ------ ----------- ------- ---------
0 | 43509376 6416 591 47709.8 0

The main problem is string cleaning. As it stands, your input DataFrame contains mostly strings because of the non-numeric characters like /, ,, and $. The first step is to clean the data and convert it to a summable type such as int or float. Then we can sum all rows.
For non-numeric fields that should be counted rather than summed ('Date'), we replace those summed strings with counts.
Also, not sure you need a single row DataFrame when a Series would have sufficed, but since it was in the requirements I did that too.
It's inelegant, but it works:
def clean(x):
if isinstance(x, str):
return(x.replace('$', '').replace(',', ''))
return(x)
data_df['Budget Delivered'] = data_df['Budget Delivered'].apply(clean).astype('float')
col_names_to_intify = ['Impressions', 'Clicks', 'Conversions']
for col in col_names_to_intify:
data_df[col] = data_df[col].apply(clean).astype('int')
sum_df = data_df.sum().to_frame().T
for col in data_df.columns:
if data_df[col].dtypes.str == '|O':
sum_df[col] = data_df[col].count()
which gives sum_df as
Date Impressions Clicks Conversions Budget_Delivered
0 5 43509376 6416 591 47709.81

Uniqueness Test on Dataframe column and cross reference with value in second column - Python

I have a dataframe of daily license_type activations (either full or trial) as shown below. Basically, I am trying to see the monthly count of Trial to Full License conversions. I am trying to do this by taking into consideration the daily data and the user_email column.
Date User_Email License_Type P.Letter Month (conversions)
0 2017-01-01 10431046623214402832 trial d 2017-01
1 2017-07-09 246853380240772174 trial b 2017-07
2 2017-07-07 13685844038024265672 trial e 2017-07
3 2017-02-12 2475366081966194134 full c 2017-02
4 2017-04-08 761179767639020420 full g 2017-04
The logic I have is to iteratively check the User_Email column. If the User_Email value is a duplicate, then check license_type column. If value in license_type = 'full' return 1 in a new column called 'Conversions' else return 0 in 'conversion' column. This would be the amendment to the original dataframe above.
Then group 'Date' column by month and I should have a aggregate value of monthly conversions in 'Conversion' column? Should look something like below:
Date
2017-Apr 1
2017-Feb 2
2017-Jan 1
2017-Jul 0
2017-Mar 1
Name: Conversion
below was my trial at getting the desire output above
#attempt to create a new column Conversion and fill with 1 and 0 for if converted or not.
for values in df['User_email']:
if value.is_unique:
df['Conversion'] = 0 #because there is no chance to go from trial to Full
else:
if df['License_type'] = 'full': #check if license type is full
df['Conversion'] = 1 #if full, I assume it was originally trial and now is full
# Grouping daily data by month to get monthly total of conversions
converted = df.groupby(df['Date'].dt.strftime('%Y-%b'))['Conversion'].sum()

Your sample data doesn't have the features you note you are looking for. Rather than loop (always a pandas anti-pattern) have a simple function that operates row by row
for uniqueness test I'm getting a count of use of email address first and setting the number of times it occurs on each row
your logic I've transcribed in a slightly different way.
data = """ Date User_Email License_Type P.Letter Month
0 2017-01-01 10431046623214402832 trial d 2017-01
1 2017-07-09 246853380240772174 trial b 2017-07
2 2017-07-07 13685844038024265672 trial e 2017-07
3 2017-02-12 2475366081966194134 full c 2017-02
3 2017-03-13 2475366081966194134 full c 2017-03
3 2017-03-13 2475366081966194 full c 2017-03
4 2017-04-08 761179767639020420 full g 2017-04"""
a = [[t.strip() for t in re.split(" ",l) if t.strip()!=""] for l in [re.sub("([0-9]?[ ])*(.*)", r"\2", l) for l in data.split("\n")]]
df = pd.DataFrame(a[1:], columns=a[0])
df["Date"] = pd.to_datetime(df["Date"])
df = df.assign(
emailc=df.groupby("User_Email")["User_Email"].transform("count"),
Conversion=lambda dfa: dfa.apply(lambda r: 0 if r["emailc"]==1 or r["License_Type"]=="trial" else 1, axis=1)
).drop("emailc", axis=1)
df.groupby(df['Date'].dt.strftime('%Y-%b'))['Conversion'].sum()
output
Date
2017-Apr 0
2017-Feb 1
2017-Jan 0
2017-Jul 0
2017-Mar 1

Complicated function with groupby and between? Python

Here is a sample dataset.
import pandas as pd
import numpy as np
df = pd.DataFrame({
'VipNo':np.repeat( range(3), 2 ),
'Quantity': np.random.randint(200,size=6),
'OrderDate': np.random.choice( pd.date_range('1/1/2020', periods=365, freq='D'), 6, replace=False)})
print(df)
So I have a couple of steps to do. I want to create a new column named qtywithin1mon/totalqty. First I want to group the VipNo (each number represents an individual) because a person may have made multiple purchases. Then I want to see if the orderdate is within a certain range (let's say 2020/03/01 - 2020/03/31). If so, I want to use the respective quantity on that day divided by the total quantity this customer purchased. My dataset is big so a customer may have ordered twice within the time range and I would want the sum of the two orders divided by the total quantity in this case. How can I achieve this goal? I really have no idea where to start..
Thank you so much!

You can create a new column masking quantity within the given date range, then groupby:
start, end = pd.to_datetime(['2020/03/01','2020/03/31'])
(df.assign(QuantitySub=df['OrderDate'].between(start,end)*df.Quantity)
.groupby('VipNo')[['Quantity','QuantitySub']]
.sum()
.assign(output=lambda x: x['QuantitySub']/x['Quantity'])
.drop('QuantitySub', axis=1)
)
With a data frame:
VipNo Quantity OrderDate
0 0 105 2020-01-07
1 0 56 2020-03-04
2 1 167 2020-09-05
3 1 18 2020-05-08
4 2 151 2020-11-01
5 2 14 2020-03-17
The output is:
Quantity output
VipNo
0 161 0.347826
1 185 0.000000
2 165 0.084848