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Tag: pandas

Pandas: filter on grouped and aggregated dataframe

I have a dataframe which is based on a read-in excel list. The data has multiple columns and rows with one unique identifier. I want to plot the data through a PyQT interface based on some user selection (checkboxes), but I cannot select one unique row for plotting. The data looks like this: After I get this: I can use

Concatenate columns at the end of a MultiIndex columns DataFrame

Consider the following DataFrames df : and df1: I want to concatenate the two DataFrames such that the resulting DataFrame is: What I run is pandas.concat([df1, df2, axis=1).sort_index(level=”kind”, axis=1) but that results in i.e. the column potato is appended at the beginning of df[“A”] whereas I want it appended to the end. Answer Add parameter sort_remaining=False in DataFrame.sort_index:

How to compare differently transposed data in pandas or python

I am trying to compare or merge two different data sets and I am using pandas for that. The challenge that I am facing is that data is spread across rows in the first data set (Data1) and the other data set (Data2) has the same data spread across columns, below are the screenshots. Screenshot 1st – This is Data1

Pandas cleaner syntax

I would like to replace the following syntax with a cleaner, chained syntax – perhaps using .pipe (similar to dplyr library in R): Sample dataset: Code to replace by piping: Expected output: Answer Here are two elegant ways to get your output: or

Performing calculations on DataFrames of different lengths

I have two different DataFrames that look something like this: Lat Lon 28.13 -87.62 28.12 -87.65 …… …… Calculated_Dist_m 34.5 101.7 ………….. The first DataFrame (name=df) (consisting of the Lat and Lon columns) has just over 1000 rows (values) in it. The second DataFrame (name=new_calc_dist) (consisting of the Calculated_Dist_m column) has over 30000 rows (values) in it. I want to

vlookup in pandas python

I have two dataframes I want to check if a column from first dataframe contains values that are in the column of second dataframe, and if it does, create a column and add 1 to the row where it contains a value from first column first df: A header Another header First apple Second orange third banana fourth tea desired

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