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Ceteris Lab Python teaching sample

Ceteris Lab Python teaching sample

Original classroom sample installed with Ceteris Lab for browser-safe pandas and econometrics practice.

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Structure

cross-sectional

File type

CSV

Module

Course catalog

Visibility

public

Preview table

Preview rows are shown when safely installed in the app. External or licensed files may need to be opened from the source link.

No local preview table is installed for this dataset. Use the download/source link, then load it with the Python example below.

Python loading code

import pandas as pd

# Download Ceteris Lab Python teaching sample from the dataset link first.
data = pd.read_stata("Ceteris Lab Python teaching sample.DTA")
print(data.head())

Check the variable dictionary and source documentation before dropping or recoding missing values.

Dataset AI assistant

Get help with variables, graphs, regression questions, limitations, and Python loading code.

Variables dictionary

wagenumeric_or_categorical

wage used in the Ceteris Lab Python teaching sample learning workflow.

Missing values: Review source documentation before recoding missing values.

educationnumeric_or_categorical

education used in the Ceteris Lab Python teaching sample learning workflow.

Missing values: Review source documentation before recoding missing values.

experiencenumeric_or_categorical

experience used in the Ceteris Lab Python teaching sample learning workflow.

Missing values: Review source documentation before recoding missing values.

tenurenumeric_or_categorical

tenure used in the Ceteris Lab Python teaching sample learning workflow.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.Original classroom sample installed with Ceteris Lab for browser-safe pandas and econometrics practice. Dataset records remain private and draft until source and licence review is complete.
  2. 2.Create one summary table and one graph that support an econometric question.
  3. 3.Write one limitation about measurement, missing data, or omitted variables.