I am trying to create a mapping where there are duplicates in certain columns in a dataframe. Here are two examples of dataframes I am working with: Here is what I need; 3 conditional python logic that does: when we see the first issue_status of 100 and trading_state of None, map F in the reason column. when we see the
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
having error while using ‘Series’ object has no attribute ‘ix’
refer this image (https://i.stack.imgur.com/yLm32.png)](https://i.stack.imgur.com/yLm32.png) Answer I believe .iloc is used for Series instead of .ix.
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 append dataframe to an existing excel file with some rows of data in it?
I have an excel sheet which has 150 rows of data. Now, I want to append a dataframe to that sheet without deleting or replacing the data using python. I have tried code like this, it is deleting the existing content from the excel and writing the dataframe into it. And other solutions provided here but with no outcome. Any
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
How to take sample of data from very unbalanced DataFrame so as to not lose too many ‘1’?
I have a Pandas DataFrame like below with ID and Target variable (for machine learning model). My DataFrame is really large and unbalanced. I need to make sampling on my DataFrame because it is really large Balancing the DataFrame looks like this: 99.60% – 0 0.40 % – 1 ID TARGET 111 1 222 1 333 0 444 1 …