Summary - Pandas basics

In this group you learned to create DataFrames, work with columns, and select data.

Summary: pandas basics

What you learned in this group:

The Series and DataFrame structures, read_csv/to_csv.
Building a table from a dict or a list of rows.
Selecting columns, adding computed columns, drop and rename.
Filtering with boolean masks, loc and iloc.
Exploring with head, info, describe, value_counts.

df = pd.read_csv("veri.csv")
X = df.drop(columns=["etiket"]).to_numpy()
y = df["etiket"].to_numpy()

The last two lines are the pattern for turning a pandas table into the X and y scikit-learn expects.

Exercise X and y

Create X by dropping the etiket column from df and y from the etiket column (both with to_numpy()). Print X.shape and y.shape separated by a space.

Show example solution

import pandas as pd

df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8], "etiket": [0, 1, 0, 1]})

X = df.drop(columns=["etiket"]).to_numpy()
y = df["etiket"].to_numpy()
print(X.shape, y.shape)

Test yourself

Which is the right way to remove the label column from X?
- df.drop(columns=["etiket"])
- df["etiket"]
- df.head()
- df.rename(columns={"etiket": ""})
Show answer

df.drop(columns=["etiket"]) — drop returns all remaining columns without the label; y becomes df["etiket"].
Convert the table to a NumPy array.
```
X = df.____()
```
Show answer
```
X = df.to_numpy()
```
Match the jobs with the tools.

Reading a file

read_csv
Filtering rows

df[condition]
Selecting by position

iloc
Summary statistics

describe

Next lesson: Data cleaning

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