03_np_pd_ex.py

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####### Crash Course Review Exercises 
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# Complete the tasks numbered below.
# If you get stuck, check out the solution video!
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# TASK 1: Import pandas and numpy
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import numpy as np
import pandas as pd


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# TASK 2: Set Numpy's random number generator seed to 101
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np.random.seed(101)


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# TASK 3: Create a NumPy Matrix of 100 rows by 5 columns consisting of
#         random integers from 1-100. (Keep in mind that the upper
#         limit may be exclusive.)
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mat = np.random.randint(1,101,(100,5))


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# TASK 4: Now use pd.DataFrame() to read in this numpy array as a dataframe.
#         Simple pass in the numpy array into that function to get back a
#         dataframe. Pandas will auto label the columns to 0-4
######
df = pd.DataFrame(data = mat)


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# TASK 5: Using your previously created DataFrame, use [df.columns = [...]]
#         (https://stackoverflow.com/questions/11346283/renaming-columns-in-pandas)
#         to rename the pandas columns to be ['f1','f2','f3','f4','label'].
######

df = pd.DataFrame(data = mat, columns=['f1','f2','f3', 'f4', 'label'])
df.columns = ['f1','f2','f3', 'f4', 'label']

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# TASK 6: Alright, all the other tasks were hopefully straightforward.
#         This final task will allow you to quickly check to see if you are at
#         the right level with pandas! Do the following:
#         Create a dataframe with the columns ['A','B','C','D'] with each column
#         having 50 rows of random numbers for data. The random numbers should be
#         between 0 and 100. (Hint: Use numpy to create the numbers, then pass
#         it in to pd.DataFrame(), check out the data= and index= parameters
#         for that call.)
rnum = np.random.randint(0,100,(50,4))
rnum = np.random.randint(0,100,200).reshape(50,4)

df = pd.DataFrame(data = rnum, columns = ['A', 'B', 'C', 'D'])
print(df)