Learning module · Detail

Analyze company key figures from CSV

This learning module conveys basic competencies in data analysis using Python for business key figures. It connects tabular data processing with statistical evaluation and graphical representation.

Time 45 min

Format Project

Language Python 3

Tasks 5

Preview image: Analyze company key figures from CSV

Introduction (Original excerpt)

In many companies, important business data are regularly collected and evaluated. Typical key figures include sales, costs, and profit. These data are often stored in table form, for example as a CSV file.

Didactic classification

Subjective objective

Introduction to the analysis of structured business data using real-world applications from economics. The focus is on processing tabular data with pandas and deriving central key figures such as mean values and extreme values. Learners model data as a DataFrame and access specific columns selectively. A systematic approach connects data modeling, statistical evaluation, and graphical visualization, creating a comprehensive analysis process from data import to interpretation.

Competence development

Importing and structuring CSV data into DataFrames
Accessing columns and selecting specific data
Calculating statistical key figures such as mean values
Identifying extreme values within a dataset
Interpreting business key figures
Visualizing time series with line charts
Connecting data analysis and graphical representation

Didactic added value in teaching

Practical context through economic data
Structured approach from import to evaluation
Promoting analytical thinking through data-based questions
Clear task assignment with traceable intermediate results
Transparent goal orientation through visible results
Differenciation possible through additional analyses or visualizations

Procedure of the lesson unit

Loading CSV file

In this step, the data foundation is created by reading in the CSV file into a DataFrame. An initial preview serves as control of the data structure and forms the basis for further analyses.

Didactic focus: Data import and understanding of structure
Typical challenge: Correct interpretation of tabular data

Calculate averages

Building on the loaded data, central key figures are calculated. The focus is on determining average values for sales and profit as a basic analysis size.

Didactic focus: Statistical evaluation of data
Typical challenge: Selection of relevant columns and functions

Determine most profitable month

In this step, an extreme value in the dataset is identified. The learners determine the month with the highest profit and link numerical analysis with content-related interpretation.

Didactic focus: Identification and interpretation of extreme values
Typical challenge: Correct selection and assignment of datasets

Visualize key figures

The calculated data is visualized in a line diagram. Several key figures are displayed in parallel to make developments and relationships visible.

Didactic focus: Graphical representation of time series
Typical challenge: Meaningful display of multiple data series

Perform entire analysis

Finally, all partial steps are combined into a complete analysis process. The results are consolidated and presented in a cohesive interpretation of the data.

Didactic focus: Integration of individual steps into a comprehensive process
Typical challenge: Structured linking of all program components

Work assignment (excerpt)

1. Read in CSV data and check structure
2. Calculate average values for central key figures
3. Determine the month with the highest profit
4. Display data graphically
5. Combine results and output

Example (excerpt)

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df = pd.read_csv("unternehmensdaten.csv")
avg_umsatz = df["umsatz"].mean()
max_row = df.loc[df["gewinn"].idxmax()]

This fragment shows the central connection of data access, statistical evaluation, and identification of relevant key figures.

Hints for classroom practice

The module is suitable for introducing data-based analysis with Python
Diversification can be achieved through additional key figures or extended visualizations
Results should be jointly interpreted and discussed
Extensions can include further datasets or more complex analyses
Points of connection exist to Economics, Statistics, and Data Science

This module combines structured data analysis with graphical representation and enables a complete analysis process from data collection to interpretation.

The clear sequence of work steps supports systematic implementation and facilitates result verification in class.

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