Pandas merge 3 dataframes with same columns

I have 3 dataframes where I have one string column which I want to merge on and 2 similar columns which I want to add up df1: df2: df3: I want: df4: Answer try this, first pandas.concat then groupby

Empty dataframe when filtering

I have a dataframe that looks like this: Now I’d like to filter like this: However, I am getting an empty dataframe. What am I doing wrong here? Answer Try df1.loc[df1[‘PZAE’]==”‘HAE'”] Details : the column ‘PZAE’ contains str starting and finishing by ‘ that’s why you have to include them in the condition

How to combine rows into seperate dataframe python pandas

i have the following dataset: i want to combine x y z into another dataframe like this: and i want these dataframes for each x y z value like first, second third and so on. how can i select and combine them? desired output: Answer Use GroupBy.cumcount for counter and then loop by another groupby object:

Use DataFrame column as index and append duplicates as new columns

I have a DataFrame that contains a column with dates which I’d like to use as my DataFrame’s index. The dates in that column are not necessarily unique – sometimes there might be duplicates. I wish to append duplicates as new columns. Dates that are unique can just contain NaN (or whatever) for the newly appended columns. To clarify I’ll provide an example: This will yield: What I want: The naming of the newly appended columns can be arbitrary. I don’t even know whether appending would be the right way to go about it. Maybe it’s easier to create a

Create Dataframe by calling indices of df1 that are listed in df2

I’m new to Python Pandas and struggling with the following problem for a while now. The following dataframe df1 values show the indices that are coupled to the values of df2 that should be called df2 contains the values that belong to the indices that have to be called. For example, df1 shows the value ‘0’ in column ‘Name161’. Then df3 should show the value that is listed in df2 with index 0. In this case ‘164’. Till so far, I got df3 showing the first 3 values of df2, but of course that not what I would like to

Python: Create lists from diagonal values in a dataframe

I’m trying to create a routine in Python to collect every diagonal group of values in df. Here’s a reproducible example of what I’m trying to achieve: This code returns me one single list: And based on the structure of the given df, what I’m trying to achieve is: From what I’ve seen I could do this by creating a list of lists (commented in the code as dict_list, here’s the reference: Python : creating multiple lists), but I haven’t been able to put my data as shown in dict_listobject. I will appreciate any help or guide. Thank you! Answer

Turn multiple columns into two new columns in a dataframe using Pandas

I am working in a python pandas environment :D Currently, I have a dataframe that looks like this : My goal is to make the dataframe look like this : Basically, I want the last 9 column titles and values to become their own rows on 2 new columns while keeping the first 8 columns and rows the same. I am aware that this means the data will be duplicated. I saw some other answers on stackoverflow use the following code for smaller dataframes but it hasn’t worked for me : Any and all help is appreciated ! Thank you

How to use pandas to create a column that stores count of first occurrences on a group-by?

Q1. Given data frame 1, I am trying to get group-by unique new occurrences & another column that gives me existing ID count per month Expected output for unique newly added group-by ID values & for existing sum of ID values Note: Mar-2020 ID_Count is ZERO because ID 1, 2, and 3 were present in previous months. Note: Existing count is 0 for Jan-2020 because there were zero IDs before Jan. The existing count for Feb-2020 is 1 because before Feb there was only 1. Mar-2020 has 3 existing counts as it adds Jan + Feb and so on Answer

Count Number of Rows within Time Interval in Pandas Dataframe

Say we have this data: I want to count, for each year, how many rows (“index”) fall within each year, but excluding the Y0. So say we start at the first available year, 1990: How many rows do we count? 0. 1991: Three (row 1, 2, 3) 1992: Four (row 1, 2, 3, 4) … 2009: Four (row 1, 2, 3, 4) So I want to end up with a dataframe that says: My attempt: But the result doesn’t look right. Appreciate any help. Answer you could do:

Remove commas from all columns except one

Is there a way to remove commas from all columns except 1 or 2 (here, just date) in general code? (I have 20 columns in reality.) Expected output: Answer Use DataFrame.replace on columns of dataframe excluding the columns from exclude list: Result: