Pandas Basics
Here we import numpy, pandas as:
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| import numpy as np
import pandas as pd
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Basic data structures in pandas
pandas provides two types of classes fro handling data:
Series: a one-dimansional labeled array holding data of any type, such as integers, strings, Python objects, etc.
DataFrame: a two-dimensional data structure that holds data like a two-dimension array or a table with rows and columns.
Object creation
Creating a Series by passing a list of values, letting pandas create a default RangeIndex
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| >>> import numpy as np
>>> import pandas as pd
>>> s = pd.Series([1, 3, 5, np.nan, 6, 8])
>>> s
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
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Creating a DataFrame by passing a NumPy array with a datetime index using date_range() and labeled columns:
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| >>> dates = pd.date_range("20260909", periods=6)
>>> dates
DatetimeIndex(['2026-09-09', '2026-09-10', '2026-09-11', '2026-09-12',
'2026-09-13', '2026-09-14'],
dtype='datetime64[ns]', freq='D')
>>> df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list("ABCD"))
>>> df
A B C D
2026-09-09 0.956212 0.496608 1.031761 0.439333
2026-09-10 -0.283260 0.644414 -0.352740 -0.617138
2026-09-11 0.667249 1.570810 -1.711173 1.114610
2026-09-12 -0.324996 -0.441097 0.821568 -0.425018
2026-09-13 0.981316 0.504753 1.098882 0.498294
2026-09-14 0.433741 0.992282 -0.737600 1.621099
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