DataDaze

Upcasting Link to official Docs Have you ever run into a scenario where you have set your column type to int but when you go to display it either in a report or visualization it comes out a float? This happens because of something called upcasting. "Types can potentially be upcasted when combined with other types, meaning they are promoted from the current type (e.g. int to float)." Lets start with a DataFrame of 1 column and 8 rows where the values are random numbers from the normal distribution. >>> import pandas as pd >>>   import numpy as np >>>   df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32') >>>   df1    A 0  0.406792 1  0.810450 2  1.161985 3 -1.402411 4  1.385434 5 -1.091746 6  0.018586 7 -0.606741 Now lets create a 3x8 DataFrame >>>   df2 = pd.DataFrame( dict(                  A =...

Merge, Join, Concat in Pandas First thing, go skim the official Pandas docs . It is pretty straightforward instructions on how to perform merge, join, concat using pandas. It has everything you need to know, but it is written very dryly and covers a lot of cases I rarely come across in my day to day, so I'll try to summarize quickly. I'll use their DataFrame in my notes below. df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],                     'B': ['B0', 'B1', 'B2', 'B3'],                     'C': ['C0', 'C1', 'C2', 'C3'],                     'D': ['D0', 'D1', 'D2', 'D3']},                     index=[0, 1, 2, 3])       df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],                     'B': ['B4', 'B5', '...