What is the most efficient way (using the least amount of lines possible) to locate and drop multiple strings in a specified column? Information regarding the .tsv dataset that may help: ‘tconst’ = movie ID ‘region’ = region in which the movie was released in ‘language’ = language of movie Here is what I have right now: I am trying
Tag: pandas
Pandas find most bought item given ClientID ItemID ItemQuantity
Among the columns of my DataFrame I have ClientID CartID FoodID Quantity, I would like to find what is the food that the client has bought the most. I tried this: But got a completely wrong output: EDIT: I also tried but this results in a pair (ClientID, Quantity of the most bought food), I need (Client, FoodID) Answer First
How to choose only the “male” attribute from a newly compiled dataframe?
I am working with the following dataframe which I created from a much larger csv file with additional information in columns not needed: df_avg_tot_purch = df_purchase_data.groupby([“SN”, “Gender”])[“Price”].agg(lambda x: x.unique().mean()) df_avg_tot_purch.head() This code results in the following: SN Gender Adairialis76 Male 2.28 Adastirin33 Female 4.48 Aeda94 Male 4.91 Aela59 Male 4.32 Aelaria33 Male 1.79 Name: Price, dtype: float64 I now need
Saving a new list from a a larger list using a loop
I have a large list, which I am pulling out every nth value from the first value up to the mth value to create new lists and I am using a for loop. My question is, how do I create a new list variable each time through the for loop? By way of simple example, I have the list: Which
Pandas add missing weeks from range to dataframe
I am computing a DataFrame with weekly amounts and now I need to fill it with missing weeks from a provided date range. This is how I’m generating the dataframe with the weekly amounts: Which outputs: If a date range is given as start=’2020-08-30′ and end=’2020-10-30′, then I would expect the following dataframe: So far, I have managed to just
Python : Dropping specific rows in a dataframe and keep a specif one
Let’s say that I have this dataframe I want to reduce this dataframe ! I want to reduce only the rows that contains the string “info” by keeping the ones that have the highest level in the column “Group”. So in this dataframe, it would mean that I keep the row “ID_info_1” in the group 4, and “ID_info_1” in the
Applying lambda to whole dataframe with if condition
I have a df that looks like this: I want to calculate the mean of the columns where A>0 so that my df would look like this: I use: But get: TypeError: ‘float’ object is not subscriptable I also tried But get: KeyError: False Which is produced by the x[‘A’]>0 mask. And: I couldn’t find a solution how can I
Difference of letting DataFrame’s column
I don’t know the difference of two ways that I let columns of DataFrame. the codes are here: when I printed A[‘ftr3’] to see elements of ftr3 of A, there was no problem. But when I printed B[‘ftr3’], the problem occured: Moreover, the reason I’m confused with this result was that print(A) and print(B) prints exactly same results. the results
Get a week startdate from week number for entire dateframe in python
I am looking for week start date for entire date frame , with format of dd-mm-yyyy, Below week number :(src_data[‘WEEK’]) code : Output : Thanks in advance Answer You can add a year and weekday as strings and parse to_datetime with the appropriate directives (see also here). If desired, convert to string with strftime:
How to fill a column with the sum of another column and the previous value of the same column?
I am building a financial model in Python. To do so, I need to calculate the “tax carry forward”. This position consists of the “EBT” plus the “tax carry forward” of the previous period. In the first period, the “tax carry forward” is equal to “EBT”. I am currently trying to solve this with the df.shift() function: df2[“carry forward”] =