I have these two initial tables: Table1: CustID StartTime EndTime Area 1 12/1/2022 4:00:00 PM 12/1/2022 4:05:00 PM ABC 2 12/1/2022 4:02:00 PM 12/1/2022 4:03:00 PM ABC Table2: Area StartTime EndTime ABC 12/1/2022 4:01:26 PM 12/1/2022 4:02:00 PM ABC 12/1/2022 4:02:05 PM 12/1/2022 4:02:55 PM ABC 12/1/2022 4:04:10 PM 12/1/2022 4:05:00 PM I need to end up with this: Table3:
Updating A List Word Counter
I’m creating a program that counts letters. I created two dictionaries that both have the same word and, because they are the same exact word, both have the same counter. I want to know how to merge the two dictionaries so that it updates the counters as well, but I consistently receive the result “NONE.” Answer You can concatenate strings
Why do I have more row after the left merge and drop_duplciates()?
iso_selected.shape gives the result: (257, 2) gravity.shapegives the result: (4428288, 79) and I merge them in the following way: gravity1 = gravity.merge(iso_selected, how=”left”, left_on = “iso3_o”, right_on=”iso3″).drop_duplicates() gravity1.shape gives the result: (4571136, 81) Why would I have more rows than 4428288 ? Answer You need remove duplicates before DataFrame.merge by column iso3_o:
Why does pandas.DataFrame.merge return dataframes with different column types than the input dataframes?
Slightly expanding the Example 1: Merge on Multiple Columns with Different Names, results in the following Python code using Pandas pandas.DataFrame.merge: The resulting output (I’ve added line numbers): Notice the type of a2 and d columns in the resulting df_merge dataframe on lines 24 through 27 have changed from the original int64 to float64. Why would it need to change
Similar to pivot table in Python
Here is a dataframe data_1. I want to make this data_1 as follows: I tried pivot_table ,but the output is not the same as I expected. Moreover, I need to save the data_1 as csv file, but the there are no columns id and date in the csv file that I made. Is there any method to change the data_1
Pandas merge only on where condition
Please help with logic on applying merge function only where condition is met. In below example: merge should be applicable only when, np.where name = John, else show 0 Expected result: TIA Answer use merge and select the good rows of your df2.
Merge two rows and put the results in the same columns
I would like to join two files by key but I would like some columns to be joined together for example: File1: File2: I would like to merge with primary key List (from file1) and Cod (from file2), to get: I think we need something like a left join and an agragation but I don’t know how. In the final
How to do left join with larger table, keeping left tables size?
I have a dataframe1: and dataframe2: i want to join type column to dataframe1 by id to get: How could I do that? as you see output table is same shape as dataframe1? but when i use pd.merge output is larger Answer Try this: Output:
Replace column values between two dataframes according to index
I have a dataframe named calls, each call is recorded by a date (calls)[datetime]. each call has an answer status (calls)[Status]. A second Dataframe named NewStatus with same column (NewStatus)[datetime] and the column (NewStatus)[New_Status] that I want to replace in the first dataframe with a date join Desired result is the following for calls : datetime Status 2021-11-22 18:47:02 AB_CL_NO
How to efficiently join a small table to a large one on python
I have two tables similar to these: My goal is to multiply df[‘x’] by df[‘y’] based on id. My current solution works, but it seems to me that there should be a more efficient/elegant way of doing this. This is my code: Answer You can use map to recover the multiplication factor from df_2, then mul to the x column: