I have a task here I have a data frame containing data about visits in a particular site.
Here's a sample:
visitsite
userid
timeonsite
facebook.com
kahy68
91973
facebook.com
jjsga12
2895
I need to create cohorts(groups) based on timeonsite(presented in seconds) column. I need also to calculate how many users are in each cohort and what is their share out of all users.
An output example:
visitdurationcohort
1000-2000
2000-3000
3000-5000
5000+
usersquantity
1383
9973
3899
684
shareofusers
7%
60%
30%
3%
So i found exampkes on how to create cohorts out of a specific value (a month of registartion for example), but not in how to create a range cohort.
I will apreciate any help :)
As per #raymond-kwok:
bins = [0,1000,2000, 3000, 5000,10000]
df1 = df.groupby(pd.cut(df["timeonsite"], bins)).count()
df1 = df1[["userid"]]
df1["shareofusers"] = df1["userid"]/(df1["userid"].sum())
df1 = df1.T
Related
Lets say i have Dataframe, which has 200 values, prices for products. I want to run some operation on this dataframe, like calculate average price for last 10 prices.
The way i understand it, right now pandas will go through every single row and calculate average for each row. Ie first 9 rows will be Nan, then from 10-200, it would calculate average for each row.
My issue is that i need to do a lot of these calculations and performance is an issue. For that reason, i would want to run the average only on say on last 10 values (dont need more) from all values, while i want to keep those values in the dataframe. Ie i dont want to get rid of those values or create new Dataframe.
I just essentially want to do calculation on less data, so it is faster.
Is something like that possible? Hopefully the question is clear.
Building off Chicodelarose's answer, you can achieve this in a more "pandas-like" syntax.
Defining your df as follows, we get 200 prices up to within [0, 1000).
df = pd.DataFrame((np.random.rand(200) * 1000.).round(decimals=2), columns=["price"])
The bit you're looking for, though, would the following:
def add10(n: float) -> float:
"""An exceptionally simple function to demonstrate you can set
values, too.
"""
return n + 10
df["price"].iloc[-12:] = df["price"].iloc[-12:].apply(add10)
Of course, you can also use these selections to return something else without setting values, too.
>>> df["price"].iloc[-12:].mean().round(decimals=2)
309.63 # this will, of course, be different as we're using random numbers
The primary justification for this approach lies in the use of pandas tooling. Say you want to operate over a subset of your data with multiple columns, you simply need to adjust your .apply(...) to contain an axis parameter, as follows: .apply(fn, axis=1).
This becomes much more readable the longer you spend in pandas. 🙂
Given a dataframe like the following:
Price
0 197.45
1 59.30
2 131.63
3 127.22
4 35.22
.. ...
195 73.05
196 47.73
197 107.58
198 162.31
199 195.02
[200 rows x 1 columns]
Call the following to obtain the mean over the last n rows of the dataframe:
def mean_over_n_last_rows(df, n, colname):
return df.iloc[-n:][colname].mean().round(decimals=2)
print(mean_over_n_last_rows(df, 2, "Price"))
Output:
178.67
I have dataframe consists of accountId, date , return value of that account on that date, and inflation rate on that date.
Date column demonstrates that how long account has been in system, for example accountId 1 get into system on 2016-01 and get out on 2019-11.
formula:
df["Inflation"] = ((1+ df["Inflation"]).cumprod() - 1) * 100
I want to apply this formula to the all accounts but here is the problem.
When I have dataframe consists of only one account it's too easy to apply formula, but when I create a dataframe consists of all users(as I indicated in image) I don't want to apply that formula simply, because every account has different date interval some of them get into system 2016 some of them 2017.
You can imagine like this, let's suppose I have dataframe of all accounts, for example df1 for account1 df2 for account2 and so on. And I want to apply that formula to each dataframe individually, and finally I want to merge all of them and have one dataframe consists of all accounts.
df["Inflation2"] = ((1+df.groupby(["AccountId","Inflation"])).cumprod()-1) * 100
I tried this code but It gives me error like "unsupported operand type(s) for +: 'int' and 'DataFrameGroupBy'"
Thanks in advance...
I solved it as follows:
df["Inflation"] = df.groupby(["AccountId"]).Inflation.apply(lambda x: (x + 1).cumprod()-1) * 100
I have 4 columns: Country, Year, GDP Annual Growth and Field Size in MM Barrels.
I am looking for a way to create a loop function that generates the mean GDP growth values over the 5 years following the discovery of a field ("Field Size MM Barrels"). Example: In 1961 a discovery was made in Algeria and its size is 2462. What is the average GDP annual growth value over the next following 5 years (1962-1967)?.
NaN refers to years where no discoveries were made in this case. I would like the loop to add the mean value each time in a column next to Field Size. Any idea how to do that?
Country,Year,GDP Annual Growth,Field_Size_MM_Barrels
Algeria,1961,-13.605441,2462.0
Algeria,1962,-19.685042,2413.0
Algeria,1963,34.313729,NaN
Algeria,1964,5.839413,NaN
Algeria,1965,6.206898,500.0
Yemen,2016,-13.621458,NaN
Yemen,2017,-5.942320,NaN
Yemen,2018,-2.701475,NaN
Divided Neutral Zone: Kuwait/Saudi Arabia,1963,NaN,832.0
Divided Neutral Zone: Kuwait/Saudi Arabia,1967,NaN,1566.0
# read in with
df = pd.read_clipboard(sep=',')
If you could include a sample of the dataframe (say first 20 rows) then it will help answer/test answers. Here's a possible starting point:
# create a list for average GDP values
average = []
# go over all rows in df.values
for row_id in range(1, len(self.df.values)):
test = self.df.iloc[row_id]["Field Size MM Barrels"]
if (test == 'NaN'):
row_list = []
# create a row list to average over:
for i in range(1+row_id,6+row_id):
row_list.append(i)
average = df[["GDP"]].iloc[row_list].mean(axis=0)
I have a dataset with daily sales of two products for the first 10 days of their release. The dataframe below shows a single and dozens of items being sold per day for each product. Its believed that no dozens product was sold before a single item of the product had been sold. The two products (Period_ID) has expected number of dozens sale.
d = {'Period_ID':['A12']*10, 'Prod_A_Doz':[1.2]*10, 'Prod_B_Doz':[2.4]*10, 'A_Singles':[0,0,0,1,1,2,2,3,3,4], 'B_Singles':[0,0,1,1,2,2,3,3,4,4],
'A_Dozens':[0,0,0,0,0,0,0,1,1,1], 'B_Dozens':[0,0,0,0,0,0,1,1,2,2]}
df = pd.DataFrame(data=d)
QUESTION
I want to construct a descriptive analysis in which one of my questions is to figure out how many single items of each product sold in average before a dozen was sold the 1st time, 2nd time,..., 10th time?
Given that df.Period_ID.nunique() = 1568
Modifying the dataset for sales per day as oppose to the above cumulative sales and using Pankaj Joshi solution with small alteration,
print(f'Average number of single items before {index + 1} dozen = {df1.A_Singles[:val+1].mean():0.2f}')
d = {'Period_ID':['A12']*10, 'Prob_A_Doz':[1.2]*10, 'Prod_B_Doz':[2.4]*10, 'A_Singles':[0,0,0,1,0,1,0,1,0,1], 'B_Singles':[0,0,1,0,1,0,1,0,1,0],
'A_Dozens':[0,0,0,0,0,0,0,1,0,0], 'B_Dozens':[0,0,0,0,0,0,1,0,1,0]}
df1 = pd.DataFrame(data=d)
# For product A
Average number of single items before 1 dozen = 0.38
# For product B
6
Average number of single items before 1 dozen = 0.43
8
Average number of single items before 2 dozen = 0.44, But I want this to be counted from the last Dozens of sales. so rather 0.44, it should be 0.5
The aim is once I have the information for each Period_ID then i will take the average for all df.Period_ID.nunique() (= 1568) and try to optimise the expected number of 'Dozens' sale for each product given under the col Prod_A_Doz and Prod_B_Doz
I would appreciate all the help.
Here is how I will go about it:
d = {'Period_ID':['A12']*10, 'Prob_A_Doz':[1.2]*10, 'Prod_B_Doz':[2.4]*10, 'A_Singles':[0,0,0,1,1,2,2,3,3,4], 'B_Singles':[0,0,1,1,2,2,3,3,4,4],
'A_Dozens':[0,0,0,0,0,0,0,1,1,1], 'B_Dozens':[0,0,0,0,0,0,1,1,2,2]}
df1 = pd.DataFrame(data=d)
for per_id in set(df1.Period_ID):
print(per_id)
df_temp = df1[df1.Period_ID == per_id]
for index, val in enumerate(df_temp.index[df_temp.A_Dozens>0]):
print(val)
print(f'Average number of single items before {index} dozen = {df_temp.A_Singles[:val].mean():0.2f}')
print(f'Average number of single items before {index} dozen = {df_temp.B_Dozens[:val].mean():0.2f}')
I am trying to do an analysis where I am trying to create two similar samples based on three different attributes. I want to create these samples first and then do the analysis to see which out of those two samples is better. The categorical variables are sales_group, age_group, and country. So I want to make both samples such as the proportion of countries, age, and sales is similar in both samples.
For example: Sample A and B have following variables in it:
Id Country Age Sales
The proportion of Country in Sample A is:
USA- 58%
UK- 22%
India-8%
France- 6%
Germany- 6%
The proportion of country in Sample B is:
India- 42%
UK- 36%
USA-12%
France-3%
Germany- 5%
The same goes for other categorical variables: age_group, and sales_group
Thanks in advance for help
You do not need to establish special procedure for sampling as one-sample proportion is unbiased estimate of population proportion. In case you have, suppose, >1000 observations and you are sampling more than, let us say, 30 samples the estimate would be quite exact (Central Limit Theorem).
You can see it in the simulation below:
set.seed(123)
n <- 10000 # Amount of rows in the source data frame
df <- data.frame(sales_group = sample(LETTERS[1:4], n, replace = TRUE),
age_group = sample(c("old", "young"), n, replace = TRUE),
country = sample(c("USA", "UK", "India", "France", "Germany"), n, replace = TRUE),
amount = abs(100 * rnorm(n)))
s <- 100 # Amount of sampled rows
sampleA <- df[sample(nrow(df), s), ]
sampleB <- df[sample(nrow(df), s), ]
table(sampleA$sales_group)
# A B C D
# 23 22 32 23
table(sampleB$sales_group)
# A B C D
# 25 22 28 25
DISCLAIMER: However if you have some very small or very big proportion and have too little samples you will need to use some advanced procedures like Laplace smoothing