I am building a simple user-based recommendation system using 10M MovieLens dataset. While calculating the Pearson Correlation, the enormous size of the data (69878 row, 10677 cols) overwhelms my memory (16GB), thus it gives me a memory error and stops.

When I’create filtered Series or Dataframe object I get filtered indices too:

I have a dataframe like the one below, where I have a daily count of points for each team. However, it’s a tough task to earn points and on many days the points stay the same. Since I’m turning the dataframe into a chart, I want to remove the rows where the point values are the same as that of the previous day. So in this case we keep row 0, row 1 is the same so we omit it, then keep row 2 because it’s different from row 1.

I have time series data that looks like this:

Hi all I want to take the differences between two dataframes,

I’m trying to compute the moving average divergence convergence (MACD) which is a technical indicator in trading. To compute MACD we have to find out exponential moving average over a certain period or a time window n(I will be providing the procedure, code on how the moving average and a sample input before the signal column is computed). The signal values range from -10 to 10. I kept getting KeyError: ‘close’. I could not understand how to proceed further please do let me know how I can correct this.

Here is my base DF I am working with.

I tried to combine and sum 2 Excel by Python but when I used “for loop”, I found an error “unsupported operand type(s) for +: ‘float’ and ‘str'”

I’m working with a DataFrame in Python using pandas, and I’m trying to apply multiple conditions to filter rows based on temperature values from multiple columns. However, after applying my conditions and using dropna(), I end up with zero rows even though I expect some data to meet these conditions.

How do I add a color column to the following dataframe so that color='green' if Set == 'Z', and color='red' otherwise?