pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]})Series is a single column of a DataFrame, it forms a list
pd.Series([1, 2, 3, 4, 5])
Meaning of this (129971, 14) when running command df.shape is “a DataFrame has 130,000 records split across 14 different columns, which almost 2 million entries”
iloc -> index-based selection,
[row, column]reviews.iloc[:, 0] to retrieve all rows of the first columnreviews.iloc[:3, 0] to retrieve rows from first(0) to third(2), fourth (3) not countedreviews.iloc[1:3, 0] to retrieve rows from 2nd(1) and 3rd(2)reviews.iloc[-5:] to retrieve the last 5 rowsloc -> label-based selection
reviews.loc[0, 'country'] to retrieve the first row of country columniloc compared to loc, because it uses index position instead. But loc provide easier ways to do things since it uses column name.reviews.loc[reviews.country == 'Italy'] -> use loc for conditional selectionreviews.loc[(reviews.country == 'Italy') & (reviews.points >= 90)] for more than 1 condition, use the ampresand (&) symbol, just like SQL but it’s only 1.or we can change it to single pipe (|) symbolreviews.loc[reviews.country.isin(['Italy', 'France'])] to select country rows that states only Italy and France values.iloc uses the Python stdlib indexing scheme, where the first element of the range is included and the last one excluded. So 0:10 will select entries 0,...,9.loc, meanwhile, indexes inclusively. So 0:10 will select entries 0,...,10.reviews['critic'] = 'everyone'reviews['index_backwards'] = range(len(reviews), 0, -1)Map function -> it uses a single value from the Series and return a transformed version of that value, for example reviews.points.map(lambda p: p - 10) it means the points column value will be reinterpreted as p subtracted with 10 for the whole rows.
Apply function -> similarly with map by function, what makes it differs is apply uses calling a custom method/function for each row and transform the whole DataFrame, for example reviews.points.apply(some_method_name, axis='columns')