I am having a MongoDB aggregate result: { "healthDetails": [ { "problem": "Fever", "note": "normal" }, { "problem": "Allergy", "note": "critical" } ] } I need to convert the above array like this: { "healthDetails": "Fever – normal, Allergy – critical" } Need to remove square brackets and double quotes and concat the values by… Read More Convert array of objects to string

Let import pandas as pd df = pd.DataFrame( { ‘a’: [‘A’, ‘A’, ‘B’, ‘B’, ‘B’, ‘C’], ‘b’: [True, True, True, False, False, True] } ) print(df) groups = df.groupby(‘a’) # "A", "B", "C" agg_groups = groups.agg({‘b’:lambda x: all(x)}) # "A": True, "B": False, "C": True agg_df = agg_groups.reset_index() filtered_df = agg_df[agg_df["b"]] # "A": True, "C":… Read More In Pandas, how to retrieve the rows which created each group, after aggregation and filtering?

I was trying to do update and sum up the column value. await single_sku_db.collection("test").updateOne( { _id: ObjectId(id) }, { $push: { report: { $each: [ { name: name, views, }, ], $position: 0, }, }, $set: { report_status: "Completed", total_views: { $sum: "$report.views"}, }, } I cant sum the report.views like this, will get this… Read More Updating data with $set and $sum mongodb

I have data like this, it’s output of a groupby: numUsers = df.groupby(["user","isvalid"]).count() count user isvalid 5 0.0 1336 1.0 387 But I need to have count of count_valid and count_invalid columns for each user, like this: count_valid count_invalid user 5 387 1336 How can I do it in optimized way in Pandas? >Solution :… Read More Pandas merging value of two rows in columns of a single row

I have Pandas DataFrame like below (I can add that my DataFrame is definitely bigger, so I need to do below aggregation only for selected columns): ID | COUNT_COL_A | COUNT_COL_B | SUM_COL_A | SUM_COL_B —–|————-|————-|———–|———— 111 | 10 | 10 | 320 | 120 222 | 15 | 80 | 500 | 500 333… Read More How to create 2 new column in DataFrame based on the highest values in rest of column with appropriate prefix in Python Pandas?

I have a large dataset with similar data: >>> df = pd.DataFrame( … {‘A’: [‘one’, ‘two’, ‘two’, ‘one’, ‘one’, ‘three’], … ‘B’: [‘a’, ‘b’, ‘c’, ‘a’, ‘a’, np.nan]}) >>> df A B 0 one a 1 two b 2 two c 3 one a 4 one a 5 three NaN There are two aggregation functions… Read More Is there pandas aggregate function that combines features of 'any' and 'unique'?

I have following dataframe in python meddra_id meddra_label soc cross_ref soc_term 2 10000081 Abdominal pain 10017947 http://snomed.info/id/21522001 Gastrointestinal disorders 3 10017999 Gastrointestinal pain 10017947 http://snomed.info/id/21522001 Gastrointestinal disorders 15 10000340 Abstains from alcohol 10041244 http://snomed.info/id/105542008 Social circumstances 35 10001022 Acute psychosis 10037175 http://snomed.info/id/69322001 Psychiatric disorders 36 10061920 Psychotic disorder 10037175 http://snomed.info/id/69322001 Psychiatric disorders I would like… Read More Group by and aggregate the values in pandas dataframe

I have a pandas DataFrame of the following format: Input: X [OTHER_COLUMNS] version branch v1 overall 2475.0 -1 . A 1712.5 1 . B 257.5 2 . C 392.5 2 D 112.5 3 v2 overall 2475.0 -1 A 2341.5 1 B 95.0 2 C 38.5 2 v3 overall 2475.0 -1 A 2000.0 1 B 475.0… Read More groupby in pandas with custom function over a subset of rows in each group

I have data resembling the following structure, where the when variable denotes the day of measurement: ## Generate data. set.seed(1986) n <- 1000 y <- rnorm(n) when <- as.POSIXct(strftime(seq(as.POSIXct("2021-11-01 23:00:00 UTC", tryFormats = "%Y-%m-%d"), as.POSIXct("2022-11-01 23:00:00 UTC", tryFormats = "%Y-%m-%d"), length.out = n), format = "%Y-%m-%d")) dta <- data.frame(y, when) head(dta) #> y when #>… Read More Aggregate daily data into weeks

I have a data frame with two columns df = DataFrame.from_records([ {"time": 10, "amount": 200}, {"time": 70, "amount": 1000}, {"time": 10, "amount": 300}, {"time": 10, "amount": 100}, ]) I want to, given a period of time 80ms, calculate the max amount that is possible, in this case, the output should be 1300 because, in this… Read More Pandas aggregate two columns at max