Menu
I want to split the following dataframe based on column ZZ
df = N0_YLDF ZZ MAT 0 6.286333 2 11.669069 1 6.317000 6 11.669069 2 6.324889 6 11.516454 3 6.320667 5 11.516454 4 6.325556 5 11.516454 5 6.359000 6 11.516454 6 6.359000 6 11.516454 7 6.361111 7 11.516454 8 6.360778 7 11.516454 9 6.361111 6 11.516454 As output, I want a new DataFrame with the N0_YLDF column split into 4, one new column for each unique value of ZZ. How do I go about this? I can do groupby, but do not know what to do with the grouped object.
445
Step 1: split the data into groups by creating a groupby object from the original DataFrame; Step 2: apply a function, in this case, an aggregation function that computes a summary statistic (you can also transform or filter your data in this step); Step 3: combine the results into a new DataFrame.
What is the GroupBy function? Pandas' GroupBy is a powerful and versatile function in Python. It allows you to split your data into separate groups to perform computations for better analysis.
You can also reset_index() on your groupby result to get back a dataframe with the name column now accessible. If you perform an operation on a single column the return will be a series with multiindex and you can simply apply pd. DataFrame to it and then reset_index. Show activity on this post.
gb = df.groupby('ZZ') [gb.get_group(x) for x in gb.groups] There is another alternative as the groupby returns a generator we can simply use a list-comprehension to retrieve the 2nd value (the frame).
dfs = [x for _, x in df.groupby('ZZ')] If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
