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Tag: dataframe

Visualize how multiple categorical values differ across rows and columns in a dataframe

I have the following DataFrame where each column represents a categorization algorithm for the items in the index (a,b, …) I would like to reorder the category names in each column so that I can better assess whether the index items are being categorised similarly across columns. Is there a way to visualise how the categories differ across columns? Something

Find value smaller but closest to current value

I have a very large pandas dataframe that contains two columns, column A and column B. For each value in column A, I would like to find the largest value in column B that is less than the corresponding value in column A. Note that each value in column B can be mapped to many values in column A. Here’s

Series Dataframe to List and Keeping Dates

This code outputs dates along with values as a series. I wanted to manipulate the values but I ended up losing the dates. Series Dataframe: Series to List: How can I keep the dates along with the new values? Answer Notice that I used copy() so fgpd doesn’t get modified. The range is set to start in 1 because I

Add timedelta to a date column above weeks

How would I add 1 year to a column? I’ve tried using map and apply but I failed miserably. I also wonder why pl.date() accepts integers while it advertises that it only accepts str or pli.Expr. A small hack workaround is: but this won’t work for months or days. I can’t just add a number or I’ll get a: Most

Pandas Reading txt file as int instad of hex (str)

I am currently trying to use Pandas to sort through Can-Bus data however when I try to make a DataFrame with 2 similar text files, I get two completely different DataFrames. When I make a data frame with the txt file “CANDUMP With Codes.txt” I get the following DataFrame: When I make a DataFrame with the txt file “ID 00d0.txt”

Number Formatting in DataFrame

How can I format a subset of a DataFrame according to a custom formatting logic? Before: Country Last Previous Abs. Change 0 United States 8.60 8.30 0.30 1 Japan 2.50 2.50 0.00 2 China 2.00 2.10 -0.10 3 United Kingdom 9.10 9.00 0.10 4 Euro Area 8.10 7.40 0.70 After: Country Last Previous Abs. Change 0 United States 8.6 8.3

Styling Large Pandas Dataframe

I have seen similar posts but have not found an answer that works. I am trying to style a very large Pandas dataframe. I have a function like the following. All it does is assign a red background to negative values and a green background to positive values. I style the entire dataframe: And finally output to HTML: The problem

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