Notes for Pandas

Wed, 09 Sep 2026 11:30:40 +0800

Pandas Basics

Here we import numpy, pandas as:

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import numpy as np
import pandas as pd

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

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

Pandas on Aminiam's Blog

Notes for Pandas

Pandas Basics

Basic data structures in pandas

Object creation