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:
Tag: dataframe
Split Business days in respective month
df1 df2 df i need to calculate 2 things column “Total” based on working days between “From” and “To” and include any holiday from df2. split the “Total” column in respective months (Jan to Dec columns) For part 1 : The column “total” in df1 is calculated using but this is not acurate and not able to include holiday(df2)in this
How to fix “only integers, slices (`:`), ellipsis (`…`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices” in this example
I have the following type of data. This is what I tried: And I got the error. Answer All you should need is:
How can I consolidate multiple rows into a single row based off their values in a Pandas Dataframe?
I have a dataframe called Traffic: I’d like to end up with a dataframe like so: Where the 4 rows are combined into 1 based off the Source. The traffic methods are then further broken up by their destinations in ascending order. If there are multiple entries from say LA->NY of type Ground, add the weights. Ground/Air columns would be
Extract sentence embeddings features with Pandas and spaCy
I’m currently learning spaCy, and I have an exercise on word and sentence embeddings. Sentences are stored in a pandas DataFrame columns, and, we’re requested to train a classifier based on the vector of these sentences. I have a dataframe that looks like this: Next, I apply an NLP function to these sentences: Now, if I understand correctly, each item
Data-frame columns into list of lists using Groupby
A data-frame and I want to transform it. The ideal result is something like: I tried: also: But not getting nearer. What’s the right way? Answer You can do: Output: If you want the list in the correct order, you would need to re-order your columns. For example: And you get:
Convert dynamic XML file to CSV file – Python
I would like to convert this XML file: to this CSV file: I can have several bodies of ID structures. I use the lxml library. I tried with the xpath method and for loop but I can only get the ID but not the rest. The problem is the second for loop, but I don’t know how to deal with
Why does read_csv skiprows value need to be lower than it should be in this case?
I have a log file (Text.TXT in this case): To read in this log file into pandas and ignore all the header info I would use skiprows up to line 16 like so: But this produces EmptyDataError as it is skipping past where the data is starting. To make this work I’ve had to use it on line 11: My
filter for rows with n largest values for each group
Context I want, for each team, the rows of the data frame that contains the top three scoring players. In my head, it is a combination of Dataframe.nlargest() and Dataframe.groupby() but I don’t think this is supported. My ideal solution is: performed directly on df without having to create other dataframes legible, and relatively performant (real df shape is 7M
How to rename a header and add values (to this column) based on other header name?
I have multiple Pandas dataframes like this one (for different years): df1= And I would like to assign to the nan the year in the Monthly Flow (2018) column, thus achieving this output: I know how to replace these nan by a specific year, one dataframe at a time. But, since I have a lot of dataframes (and will have