I try to add a new column “energy_class” to a dataframe “df_energy” which it contains the string “high” if the “consumption_energy” value > 400, “medium” if the “consumption_energy” value is between 200 and 400, and “low” if the “consumption_energy” value is under 200.
I try to use np.where from numpy, but I see that numpy.where(condition[, x, y]) treat only two condition not 3 like in my case.
Let’s assume that I have the following dataframe in pandas:
I have a Numpy array consisting of a list of lists, representing a two-dimensional array with row labels and column names as shown below:
I have a Pandas dataframe (this is only a little piece)
One of my favorite aspects of using the ggplot2 library in R is the ability to easily specify aesthetics. I can quickly make a scatterplot and apply color associated with a specific column and I would love to be able to do this with python/pandas/matplotlib. I’m wondering if there are there any convenience functions that people use to map colors to values using pandas dataframes and Matplotlib?
I have a fairly large dataset in the form of a dataframe and I was wondering how I would be able to split the dataframe into two random samples (80% and 20%) for training and testing.
I have the following dataframe:
I have a dataframe where one column is a list of groups each of my users belongs to. Something like:
How can I export a list of DataFrames into one Excel spreadsheet?
The docs for to_excel state:
I have an n-by-m Pandas DataFrame df defined as follows. (I know this is not the best way to do it. It makes sense for what I’m trying to do in my actual code, but that would be TMI for this post so just take my word that this approach works in my particular scenario.)