Installing & Importing

Before you can analyse a single IPL scorecard, pandas has to be installed on your computer and imported into your program. pandas is not part of Python's standard library, so a fresh Python installation does not have it.

Definition

Installing a package downloads it (and its dependencies, such as NumPy) into your Python environment once. Importing loads the installed package into the current program every time it runs, and import pandas as pd binds it to the short alias pd.

• Install once, import every time — installation is a one-time setup step; the import line goes at the top of every script or notebook.
• Dependencies — pip/conda automatically install what pandas needs: NumPy, python-dateutil (date parsing) and tzdata (time zones).
• Alias pd — a universal convention. Every tutorial, Stack Overflow answer and job codebase writes pd.DataFrame, pd.read_csv, etc.

Syntax — installing

# with pip (any Python installation)
pip install pandas

# upgrade to the latest version
pip install --upgrade pandas

# install a specific version
pip install pandas==3.0.2

# with conda (Anaconda / Miniconda)
conda install pandas

# inside a Jupyter / Colab notebook cell
!pip install pandas

Tip: Anaconda and Google Colab already include pandas. On this website's playground, pandas is also pre-loaded — just write import pandas as pd.

Optional extras pandas can use (install only when needed):

Syntax — importing

import pandas as pd        # the standard convention
import numpy as np         # almost always imported alongside pandas

pd.__version__             # version string, e.g. '3.0.2'
pd.show_versions()         # long report: pandas, Python, NumPy and all optional packages

Example — check that everything works

import pandas as pd
import numpy as np

print("Pandas version:", pd.__version__)
print("NumPy version :", np.__version__)

Pandas version: 3.0.2
NumPy version : 2.4.6

How it works: __version__ is a special attribute that most libraries provide. Checking it is the first thing to do when code from a tutorial behaves differently on your machine — the tutorial may have used an older version.

Example — display options

When a DataFrame is large, pandas shortens the printout with ... so your screen is not flooded:

df = pd.DataFrame({"x": range(100)})
print(df)

     x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]

You can control this with options:

pd.set_option("display.max_rows", 100)       # show up to 100 rows before shortening
pd.set_option("display.max_columns", None)   # never hide columns
pd.set_option("display.width", 120)          # characters per line before wrapping
pd.set_option("display.precision", 2)        # decimals shown for floats (display only!)
pd.get_option("display.max_rows")            # read an option
pd.reset_option("display.max_rows")          # back to default

Note: Display options only change how data is printed — display.precision does not round the actual values.

Common mistakes

• ❌ ModuleNotFoundError: No module named 'pandas' → pandas is not installed in the Python you are running. ✅ Run python -m pip install pandas with the same python that runs your script (virtual environments are a common cause).
• ❌ Naming your own file pandas.py. ✅ Python will import your file instead of the library. Rename it (e.g. ipl_analysis.py).
• ❌ import pandas and then writing pd.DataFrame. ✅ Use import pandas as pd so the name pd exists.
• ❌ pip install panda (singular). ✅ The package is pandas.

Interview tip

Q: How do you find which pandas version a script is running with, and why does it matter? A: pd.__version__ (or pd.show_versions() for everything). Behaviour changes between major versions — for example pandas 3 introduced a dedicated str dtype and Copy-on-Write — so the version explains why the same code can print different results on different machines.