Python pandas can’t add a column name because of an index
Here is dataframe I’m working with.
Here is dataframe I’m working with.
I have two dataframes like below,
I am trying to use mplcursors when plotting data from multiple pandas DataFrames (or from multiple subsets of a single DataFrame).
I have read the answers of this question and this one, as well as this one, which is somewhat redundent with the first one.
I am able to adapt the code of the documentation on Extracting data and labels from a DataFrame to using seaborn with a single DataFrame, i.e. the following works fine:
I have two ndarrays of size (m x n), and two lists of length m and n respectively. I want to convert the two matrices to a dataframe with four columns. The first two columns correspond to the m and n dimensions, and contain the values from the lists. The next two columns should contain the values from the two matrices. In total, the resulting dataframe should have m times n rows.
I’ve to write a function (column_means), that calculates the mean of each column from Dataframe and give me a list of means at the end. I’m not allowed to use the mean function .mean(), so I’m implementing the general formula of the mean: sum(x_i)/Number of elements.
I am trying to apply my own function. Below you can see the data and function.
I have a data frame “p” and a np array “b” as follow
a= {‘A’ : [1, 2,3,4], ‘B’ : [‘FOOTBALL’,’BASKETBALL’,’HANDBALL’,’VOLLEYBALL’], ‘C’ : [[5,10,15,40],[1,4],[20,10,40],[10,40]] } How can I remove the element 40 from C if B is different to FOOTBALL Like this : A B C 0 1 FOOTBALL [5, 10, 15, 40] 1 2 BASKETBALL [1, 4] 2 3 HANDBALL [20, 10] 3 4 VOLLEYBALL [10] Answers: … Read more
How can I achieve the equivalents of SQL’s IN and NOT IN?
do include small* example DataFrame, either as runnable code: