Pandas-groupby Questions

I need display in dataframe columns both the user_id and length of content_id which is a list object. but struggling to do using groupby. please

I am reposting since my last question was poorly worded. i have a table that looks like the following:

I tried to select a single value of column class from each group of my dataframe after i performed the groupby function on the

I have this code: df.droupby('type)['feature1].mean() df has 15 features from 1 to

I have a dataset where i want to make a new variable everytime 'recording' number changes. i want the new variable to include the 'duration' data

I am having a dataset of all the abstracts and the author gender. now i want to get the all the repetitions of words gender wise so that i can

Please assist. how do i get the cumsum of a pandas groupby, but my data is boolean 0 and 1. i want to treat each group of 0s or 1s as unique

I have this dataframe grouped df1 = pd.dataframe( { "name" : ["alice", "bob", "mallory", "mallory", "bob" , "mallory"] ,

Given the following dataframe: +-------+-----+-------+-----+--------+---------------------------+ | did | cid | event | oid |

I am trying to do analysis of large amount of data , first i applied groupby function to divide data in different groups. then i check some

I have a dataframe like as shown below df = pd.dataframe({ 'subject_id':[1,1,1,1,2,2,2,2,3,3,4,4,4,4,4], 'readings' :

I have a dataframe like as shown below op1 = pd.dataframe({ 'subject_id':[1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2], 'date' :

I have a dataset on which i would like to run multiple aggregation steps using. this code creates the data: import pandas as pd

I'm running into a very strange issue ever since i have ported my code from one computer to another. i'm using pandas version 0.25.1 on this

I have data coming from two groups a and b. the task is to monitor change, and if change (leap) is bigger that 4, the stage is set higher by 1.

I have a dataframe like as shown below df = pd.dataframe({ 'subject_id':[1,1,1,1,1,1,1,2,2,2,2,2], 'time_1' :['2173-04-03

I am having troubles with a code that is useful to create a pixel map, particularly in the loop that groups data in the selected area. i can't get

I have the below data set consisting of cards swiped and time when swiped. the output has to be total no of cards swiped month and year wise.

I want to aggregate some data to append to a dataframe. the following gives me the number of wins per name import pandas as pd

I ran into a problem trying to split my csv into the minimum value of csv files so each has only unique ids in it by running

I have a set of data that needs to be filtered and saved in different excel based on unique values in a column. for example, in src

I have a dataframe like as given below edited dataframe df = pd.dataframe({

I have dataframe s which is a group by object: s = df.groupby(['x','y']) i would like to take the first event in

Continuing from my

Given df that represent an events of users. index id action_id feature session_id n_page duration 1 1 null

This is my first question on this forum. i am conducting experiments in which i measure the current-voltage curve of a device applying

I have data like this: df = pd.dataframe({ 'a': ['milk', 'eggs', 'eggs', 'butter', 'butter', 'milk', 'eggs', 'eggs',

I have this pandas group by statement:

Question 1: i have a data frame with two month value columns as month1 and month2. if the value

Df_new = pd.dataframe( { 'person_id': [1, 1, 3, 3, 5, 5], 'obs_date': ['12/31/2007', 'na-na-na na:na:na', 'na-na-na na:na:na',

Basically task is that for every customer last 5 transactions should show up but it should be on basis of that customer only. df =

Given the following dataset df = pd.dataframe( { 'yearmo': ['01', '02', '01', '02', '01', '02'], 'prod': ['a',

I have some data like this: df = pd.dataframe({'x':[1,2,3,1,1,2,3,3,2], 'y':['n', 'n', 'p', 'p', 'n', 'n', 'n',

For example, if we have a list of a lot of names. how to count occurrences in a sequence? or more exactly, how to use groupby to sort the list and

I have the following sample dataset df = pd.dataframe({ 'names': ['joe', 'joe', 'joe'], 'dates': [dt.datetime(2019,6,1),

I am facing the next problem: i have groups (by id) and for all of those groups i need to apply the following code: if the distances between

I have a dataframe which can be generated using the code below df2 = pd.dataframe({'subject_id':[1,1,1,1,1,1,2,2,2,2],'colum' :

I want to groupby my data using a column (no) and keep each result of the columns date1 and

I'm new to pandas/numpy and i'm playing around to see how everything works. i'm using this dataset for the top 1000 imdb movie ratings:

Having the following df: pd.dataframe({'bool':[true,true,true, false,true,true,true], 'foo':[1,3,2,6,2,4,7]})

I'm still relatively new to pandas and i can't tell which of the functions i'm best off using to get to my answer. i have looked at pivot,

I have a dataframe, a simplified example is below: cycle sensor value 0 0 1 0.34 1 0 1 0.80 2

I have a nested loop. but my data set is very large, so i need a faster way. i believe it can be done with grouping or mapping the data in some

I have this data frame: o d r kz p 1 3 1 5 nan 1 3 2 0 nan 1 10 1 7 nan 1 10 3 1 nan 1 10

I am trying to calculate percent change (for periods greater than 1) with a shrinking window effect at the end of a series. the following

I have a pandas dataframe containing baseball fielding statistics. each row shows how many games a player has appeared at a given position over

In pandas 1.1.4, i am receiving a dataerror: no numeric types to aggregate when using an expandinggroupby. example dataset:

I have a single data frame and every row is duplicated except for two values. in all cases the corresponding duplicate has a blank value in the

I have a dataframe like below: id name cola

I'd like to filter out columns within each group of 'ticker' so that what remains are only rows exactly 1, 2, 3, etc. years before my most recent

How to groupby two keys in dictionary and get the sum of the values of the other key val.

>>> df=pd.dataframe({'order no':[71,71,71,71,71,71,71,72,72,72,72,72,72,72,73,73],'product

I am struggling to produce a pandas groupby multiindex plot the way i want. i have the following dummy pandas dataframe: data = {

I have sorted my first level of index using the following method :

I have two data, one with columns: df1 = id as hs ts a a_1 a_6 a_7

For the series s t = [0.1, 1, 2, 3, 0, 1, 2, 3, 4, 5, 0.9, 1, 2] s = pd.series(t) i would like to get the

I have a dataframe as below a b c 1 1 2 3 2 4 2 5 and i want to combine index and

When working with pandas datetimes, i'm trying to group data by the week and year. however, i have noticed some years where the last day of the

I have a following dataframe: driver_id status dttm 9f8f9bf3ee8f4874873288c246bd2d05 free

Dataframegroupby objects are output by dataframe.groupby and have the method transform. according to the

I am working with a book rating dataset of the form userid | isbn | rating 23413 1232 2.5 12321 2311 3.2 23413

I want the following data to be converted like the below expected output. values of 2nd column must be grouped and displayed in a single row based

I have to slice my dataframe into new dataframes, grouped by the destination (i'm using pandas). this is my dataframe called

a b 0 1 12 1 1 13 2 1 15 3 2 16 4 2 19 5 2 20 6 3 32 7 3 29 8 3 25 9 4 3 10 4 5 11 4 7 i

This is my dataframe: df = pd.dataframe({'sym': list('aaaaaabb'), 'order': [0, 0, 1, 1, 0, 1, 0, 1], 'key': [2, 2, 2, 2, 3, 3, 4,

I have this dataframe state county pop 1 alabama autauga county 54571 2

I have a dataframe and want to groupby one column, "company" and aggregate multiple columns and find the company with the max value for each

I have a dataset in which i have the following columns: date, id, value. i then want a running sum of the preceding 3 days (including the current

I have a csv file that i'm using pandas dataframe to manipulate. the data that i have is tweet data, and what i'm trying to do is merge the cells

I have a dataframe that consists of information about every nfl play that has occurred since 2009. my goal is to find out which teams had the most

How can i aggregate data when the order of grouped data is important? (bonus points if this can be done in an elegant vectorized

I am calculating the minimum of each group using this piece of code: df['columnname'].groupby(group_identifier).transform('min')

I have hourly readings in a dataframe of the form: date_time temp 2001-01-01 00:00:00 -1.3 2001-01-01

I have a dataset as shown below. the idea is looking at every previous 15minutes not the frequency which we use in grouper function. i want to see

I would like to replace null value of stadium attendance (affluence in french) with their means. therefore i do this to have the mean by seasons /

I have a large dataset which has among others a binary variable: transactions['has_acc_id_and_cus_id'].value_counts() 1 1295130

I have pandas dataframe like this: data = {'id_1':['a', 'a','a', 'b', 'b', 'b'], 'id_2':[1, 2, 2, 1, 1, 2],

Consider the following csv file where there is a duplicate name in "name" column: id,name,t,ca,i,c,ip

I have a dataframe that looks like this id ..... config_name config_version ... aa a 0

I have a pandas dataframe which looks like

Have df with values 0 | 1 | 2 0 sun | east | pass 1 moon | west | pass 2 mars | north |

I am trying to create a column (is_max) that has either 1 if a column b is the maximum in a group of values of column a or 0 if it is not.

On a sample dataframe data = pd.dataframe(np.random.rand(6,2), columns = list('ab')) dti = pd.date_range(start='2019-02-12',

I was looking at the pandas source code

I have a df.head() of my data frame looks like this. i'm measuring my data somewhere between 7 and 9 hz frequency and have about 100

How do i count the number of multicolumn (thing, cond=1) event occurrences prior to every (thing, cond=any)

I have some data from which i want to extract a time series of revenues (sum of dollars in different dates day over

I would like to sum the diagonal value of each year and residue, grouping by object. for example for object a will be 1 + 10 + 11 + 12 + 13. is

I have a dataframe: df1 = pandas.dataframe( { "text" : ["alice is in ", "alice is in

Let say i have an array with event and log time, like this: time event 01/01/2019 8h00 x 01/01/2019 8h10 y 01/01/2019

I'm missing something in the syntax of merging in pandas. i have the following 2 data frames: >>> dfa s_name

I have a dataset which requires missing value treatment. column missing values complaint_id

Finding the top categories of a column value based on the other column df : nationality age card category amount india

I have a data frame as below, i need to write a function which should be able to give me the below results: input parameters:

I want to drop duplicates only in particular subsets from a data frame. under each "spec" in the column "a" i want to drop duplicates, but i want

I'm trying to extract grouped row data to use values to plot it with label colors another file. my dataframe is like below.

I have dataframe like so, id,class_id,active 1,123,0 2,123,0 3,456,1 4,123,0

There are different names who have bought different fruits. i need the person name and the different fruits he has bought with the price

I have data like this: user reg ind prod a asia tele tv a asia bank phone a

Here's a sample dataframe: import pandas as pd df = pd.dataframe({'id':[1,1,1,2,2,2,3,3], 'value':[42, 89,