I have a mock dataframe, df1 with 6 columns and 5 rows, i.e., with shape (5 x 6). Each column represents the price of an area, and rows are time. Now, I want to identify – in each row – the areas with the same price as the first column “DK1”, and then being able to sum up how often
Tag: dataframe
Why file row count is more than len(dataframe)?
Good morning, I’m new to python and data analysis world, so bear with me. I’ve been trying to understand why when counting file rows it gives the right answer but after converting to dataframe and counting len(datafarme), it gives a rowcount-1. I’m sure it’s simple but I’ve googled it for about two hours and I didn’t find an answer yet,
Problem with different extension files upload at streamlit
I’m trying to let the user select what files he wants to upload, but I’m facing a problem. For example, there are two types of extension files the user can upload (csv and xlsx). After he upload his file streamlit needs to open the file and shows as a dataframe. But in code I did, I create two if’s to
Check if a row in one DataFrame exist in another, BASED ON SPECIFIC COLUMNS ONLY
I have two Pandas DataFrame with different columns number. df1 is a single row DataFrame: df2, instead, is multiple rows Dataframe: I would to verify if the df1’s row is in df2, but considering X0 AND Y0 columns only, ignoring all other columns. In this example the df1’s row match the df2’s row at index 3, that have 100 in
Fastest way to check pandas dataframe and show other elements in the other columns at the same row
If there is a list of words to check… and a data frame like What is the fastest way to find the corresponding scores for each word in the given word list? For example, 40, 20, 10 for ‘word3’. Answer To elaborate on comments above: Output: If you don’t want to set Word as index, you can also use .iloc:
Python Pandas – Lookup a variable column depending on another column’s value
I’m trying to use the value of one cell to find the value of a cell in another column. The first cell value (‘source’) dictates which column to lookup. My required output value in the ‘output’ column is a lookup of the ‘source’: Failed attempts This results in a ValueError: Wrong number of items passed 4, placement implies 1 because
Multiplying pandas columns based on multiple conditions
I have a df like this I want a output like below The goal is calculate column C by mulytiplying A and B only when the count value is “yes” but if the column People values are same that is yes for dia and no for also dia , then we have to calculate for the count value “no” I
Groupby and count only how many times customer was called at specific point of time
my problem is closely related to Groupby count only when a certain value is present in one of the column in pandas. Let’s say I have a dataframe which is sorted by not_unique_id and date_of_call. Now I want to add a new column which tells me, how often the customer was called successfully in the past. In other words: count
How format different values in pandas?
i have a columns of dataframe with 1000+ different format of values. how can i format these in order to have unified view like this 1.000.000. for example: row 1 is desiderated view row 2 10000000 should be 10.000.000 row 3 150,250 should be 150.250 row 4 0,200655 should be 200.655 Answer For your input this should work: Here we:
Create new dataframe from an existing dataframe
I have a pandas dataframe with say 6 columns. 3 of the columns are of length 5. Two of the columns are of length 2 and the last column is of length 8. The columns are randomly positioned in the dataframe. I would like to create 3 new dataframes. The first dataframe should only contain all the columns whose length