The folks over at yhat just released a cheat sheet for pandas. You can download the cheat sheet in PDF for here.
There’s a couple important functions that I use all the time missing from their cheat sheet (actually….there are a lot of things missing, but its a great starter cheat sheet).
A few things that I use all the time with pandas dataframes that are worth collecting in one place are provided below.
Renaming columns in a pandas dataframe:
df.rename(columns={'col1': 'Column_1', 'col2': 'Column_2'}, inplace=True)
Iterating over a pandas dataframe:
for index, row in df.iterrows(): * DO STUFF
Splitting pandas dataframe into chunks:
The function plus the function call will split a pandas dataframe (or list for that matter) into NUM_CHUNKS chunks. I use this often when working with the multiprocessing libary.
# This function creates chunks and returns them def chunkify(lst,n): return [ lst[i::n] for i in xrange(n) ] chunks = chunkify(df, NUMCHUNKS)
Accessing the value of a specific cell:
This will give you the value of the last row’s “COLUMN” cell. This may not be the ‘best’ way to do it, but it gets the value
df.COLUMN.tail(1).iloc[0]
Getting rows matching a condition:
The below will get all rows in a pandas dataframe that match the criteria. In addition to finding equality, you can do all the logical operators.
df[df.COLUMN == Criteria]
Getting rows matching multiple conditions:
This gets rows that match a criteria in COLUMN1 and those that match another criteria in COLUMN2
df[(df.COLUMN1 == Criteria) & (df.COLUMN2 == Criteria_2) ]