Convert unix time to readable date in pandas dataframe
I have a dataframe with unix times and prices in it. I want to convert the index column so that it shows in human readable dates.
dataframe
Pandas DataFrame: replace all values in a column, based on condition
I have a simple DataFrame like the following:
Numpy “where” with multiple conditions
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.
Reshape wide to long in pandas
Let’s assume that I have the following dataframe in pandas:
How can I split a column of tuples in a Pandas dataframe?
I have a Pandas dataframe (this is only a little piece)
How do I create test and train samples from one dataframe with pandas?
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.
Is there a way in Pandas to use previous row value in dataframe.apply when previous value is also calculated in the apply?
I have the following dataframe:
Pandas DataFrame stored list as string: How to convert back to list
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.)
Shuffle DataFrame rows
I have the following DataFrame:
What is the fastest way to upload a big csv file in notebook to work with python pandas?
I’m trying to upload a csv file, which is 250MB. Basically 4 million rows and 6 columns of time series data (1min). The usual procedure is: