A Pandas Series is a one-dimensional labeled array capable of holding any data type. It is similar to a column in a DataFrame and can be created from a list, dictionary, or scalar value.
A Series can be easily created from a Python list using the Pandas library.
import pandas as pd
data = [1, 2, 3, 4, 5]
series = pd.Series(data)
print(series)
Console Output:
0 1 1 2 2 3 3 4 4 5 dtype: int64
You can also create a Series from a dictionary, where the keys become the index.
import pandas as pd
data = {'a': 10, 'b': 20, 'c': 30}
series = pd.Series(data)
print(series)
Console Output:
a 10 b 20 c 30 dtype: int64
Accessing data from a Series is similar to accessing data from a list or dictionary.
import pandas as pd
data = [10, 20, 30, 40, 50]
series = pd.Series(data)
print(series[2]) # Access by index
Console Output:
30
Pandas Series supports vectorized operations, which means you can perform operations on the entire Series.
import pandas as pd
data = [1, 2, 3, 4, 5]
series = pd.Series(data)
print(series * 2) # Multiply each element by 2
Console Output:
0 2 1 4 2 6 3 8 4 10 dtype: int64
Pandas Series can handle missing data using NaN (Not a Number) values.
import pandas as pd
import numpy as np
data = [1, 2, np.nan, 4, 5]
series = pd.Series(data)
print(series)
Console Output:
0 1.0 1 2.0 2 NaN 3 4.0 4 5.0 dtype: float64
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