Machine learning and statistics with python

 Introduction:   Pandas is one of the most basic data processing libraries data enthusiasts learn and use frequently. We have discussed 10 most basic functions to know from pandas in a previous post. Now, although I have known and used groupby for quite a bit of time now, there are a lot of tricky things and actions around the groupby functions we need to learn, so that one can utilize groupby functions most. The basics: Now, if you are new to pandas, let's gloss over the pandas groupby basics first. groupby() is a method to group the data with respect to one or more columns and aggregate some other columns based on that. The normal syntax of using groupby is: pandas.DataFrame.groupby(columns).aggregate_functions() For example, you have a credit card transaction data for customers, each transaction for each day. Now, you want to know how much transaction is being done on a day level. Then in such a case, to know the transaction on a day level, you will want to group the data...

Problem 1: Grouper for is not 1-dimensional I was writing a nice little script in pandas where I wanted to group some data using some identifier columns. And then suddenly the error popped in the console was: Grouper for Id is not 1-dimensional. And I started investigating for it. Solution: This error comes if there are multiple copies of the same grouper column. In that case, you will find this error. grouper for bar is not 1-dimensional: source: stackoverflow Problem 2: unhashable type: numpy.ndarray This is a more generalized type of error. There are multiple instances where this error occurs; including when you try to treat a numpy ndarray object as a value or a string type object and so on. I encountered this problem when I tried to group a bunch of values in which a column contained arrays as entries. If your pandas dataframe contains array or np.ndarray as values of single cells, then this dataframe can not be further used for other different actions even like drop_du...