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Python Pandas For Your Grandpa | Section 2.4 | Series Overwriting Data

Course Contents Introduction Series 2.1 Series Creation 2.2 Series Basic Operations 2.3 Series Basic Indexing 2.4 Series Overwriting Data 2.5 Series Apply 2.6 Series Concatenation 2.7 Series Boolean Indexing 2.8 Series View Vs Copy 2.9 Series Missing Values 2.10 Series Challenges import numpy as np import pandas as pd Now that we know how to access data from a series using an index, overwriting data is pretty straight-forward.

Python Pandas For Your Grandpa | Section 2.5 | Series Apply

Course Contents Introduction Series 2.1 Series Creation 2.2 Series Basic Operations 2.3 Series Basic Indexing 2.4 Series Overwriting Data 2.5 Series Apply 2.6 Series Concatenation 2.7 Series Boolean Indexing 2.8 Series View Vs Copy 2.9 Series Missing Values 2.10 Series Challenges import numpy as np import pandas as pd .apply() Suppose you have some cool, complicated function like this one, which takes in a scalar value, x, subtracts 1 if it’s less than 1.

Python Pandas For Your Grandpa | Section 2.6 | Series Concatenation

Course Contents Introduction Series 2.1 Series Creation 2.2 Series Basic Operations 2.3 Series Basic Indexing 2.4 Series Overwriting Data 2.5 Series Apply 2.6 Series Concatenation 2.7 Series Boolean Indexing 2.8 Series View Vs Copy 2.9 Series Missing Values 2.10 Series Challenges import numpy as np import pandas as pd Let’s make some Series. rg_idx = pd.Series([1, 2, 3, 4]) int_idx = pd.Series([10, 20, 30, 40], index=[0, 1, 2, 3]) float_idx = pd.

Python Pandas For Your Grandpa | Section 2.7 | Series Boolean Indexing

Course Contents Introduction Series 2.1 Series Creation 2.2 Series Basic Operations 2.3 Series Basic Indexing 2.4 Series Overwriting Data 2.5 Series Apply 2.6 Series Concatenation 2.7 Series Boolean Indexing 2.8 Series View Vs Copy 2.9 Series Missing Values 2.10 Series Challenges import numpy as np import pandas as pd Just like NumPy arrays, you can subset a pandas Series using a boolean index. For example, if you have the Series

Python Pandas For Your Grandpa | Section 2.8 | Series View Vs Copy

Course Contents Introduction Series 2.1 Series Creation 2.2 Series Basic Operations 2.3 Series Basic Indexing 2.4 Series Overwriting Data 2.5 Series Apply 2.6 Series Concatenation 2.7 Series Boolean Indexing 2.8 Series View Vs Copy 2.9 Series Missing Values 2.10 Series Challenges import numpy as np import pandas as pd Let’s suppose you have this series, x x = pd.Series( data=[2, 3, 5, 7, 11, 13], index=[2, 11, 12, 30, 30, 51] ) and then you set a new variable, y, equal to x

Python Pandas For Your Grandpa | Section 2.9 | Series Missing Values

Course Contents Introduction Series 2.1 Series Creation 2.2 Series Basic Operations 2.3 Series Basic Indexing 2.4 Series Overwriting Data 2.5 Series Apply 2.6 Series Concatenation 2.7 Series Boolean Indexing 2.8 Series View Vs Copy 2.9 Series Missing Values 2.10 Series Challenges import numpy as np import pandas as pd One of the fundamental features of pandas is its ability to represent missing or invalid data using NaN.