The best way to do this in Pandas is to use drop: df = df.drop(‘column_name’, 1) where 1 is the axis number (0 for rows and 1 for columns.) To delete the column without having to reassign df you can do: df.drop(‘column_name’, axis=1, inplace=True) Finally, to drop by column number instead of by column label, … Read more
To select rows whose column value equals a scalar, some_value, use ==: df.loc[df[‘column_name’] == some_value] To select rows whose column value is in an iterable, some_values, use isin: df.loc[df[‘column_name’].isin(some_values)] Combine multiple conditions with &: df.loc[(df[‘column_name’] >= A) & (df[‘column_name’] <= B)] Note the parentheses. Due to Python’s operator precedence rules, & binds more tightly than … Read more
How to iterate over rows in a DataFrame in Pandas
Do you understand the concept of cross validation? In k-fold cross validation your data is split in k samples and one sample is used to test the model and k-1 samples are used to train the model. If you set k = 1 (or n = 1 in your example) you won’t be able to … Read more
Your question really needs clarifications but since they are MIA i will assume ahead. ASSUMPTIONS The column on the left comes from a list of lists. I will name that “alist_oflists”. Every time a unique inner list is found, a new integer-type identifier is attributed to it. The output can simply be a list of … Read more
I’m not sure that the sum(column2) / count(distinct column3) as C part can be done in the same single step, but you can easily do it in two steps: Demo: In [47]: df = pd.DataFrame(np.random.randint(0,5,size=(15, 3)), columns=[‘c1′,’c2′,’c3’]) In [48]: df Out[48]: c1 c2 c3 0 4 0 3 1 2 3 2 2 1 2 … Read more
You can use pandas groupby method with list comprehension which will do the JOb like Below: >>> df X Y 0 Yes 1 1 No 2 2 Yes 3 3 Yes 4 4 No 2 5 No 1 6 Yes 0 7 No 4 8 No 4 9 No 5 >>> {k: v[“Y”].tolist() for k,v … Read more
The problem is that R is trying to be “helpful”, and simplifying your data for you. The solution is to do the following (note two commas, not one): df[-1,, drop = FALSE] This will remove the specified row, and leave your data.frame otherwise untouched.
Try return min(columnrow), and change df[‘a’].apply(…) to df.apply(…)[‘a’]. (Why not df[‘a’].min()?)
you should use the float option to run on each line. df[“DAY”].astype(float) and then you can do regular math on it.