Lesson module · Detail

Visualize climate data from CSV

This module introduces basic methods of data analysis and connects working with tabular data to its graphical representation. It supports the development of competencies in handling real datasets as well as their structured evaluation and interpretation.

Time 60 minutes

Format Project

Language Python 3

Tasks 5

Preview image: Visualize climate data from CSV

Introduction (Original excerpt)

In this module, you work with a typical task from the Data Science. You load real data from a CSV file, investigate it with Python, and then represent it graphically.

Didactic classification

Subjective objective

This module introduces you to fundamental concepts of data-based analysis, particularly working with structured datasets in the form of CSV files. It introduces the use of a DataFrame as the central data structure and uses it for analyzing time series. Learners discover statistical relationships by calculating means and identifying extreme values. At the same time, the transformation of data into visual representations is conveyed, creating a connection between numerical analysis and graphical interpretation. The connection between model (dataset) and implementation (Python code) is at the center of the learning process.

Competence development

Importing and structuring tabular data with pandas
Analyzing time series based on monthly temperature values
Calculating statistical metrics such as means
Deriving extreme values from datasets
Extending data structures by additional columns
Visualizing data with line graphs
Interpreting analysis results in graphical form

Didactic added value in teaching

Clear sequence of data import, analysis, and visualization
Structured task assignment with traceable intermediate steps
Promoting systematic and data-oriented thinking
Transparent goal orientation through visible results
Connectivity to topics like statistics and Data Science
Differentiation through extensions and additional analysis tasks

Flow of the lesson unit

Load CSV data

In this step, the dataset is loaded from a CSV file into a DataFrame and an initial overview is created. This ensures that the data has been correctly read in and serves as the basis for all further analyses.

Didactic focus: Understand and verify data structure
Typical challenge: Handling tabular data and column access

Calculate annual average

Average values are calculated for multiple data series to gain an overview of temperature development. This step deepens the understanding of statistical evaluations in tabular data structures.

Didactic focus: Calculate statistical key figures
Typical challenge: Selecting and processing multiple columns

Determine warmest month

By adding a new column, maximum values per month are determined and the extreme value is identified. This shows how data can be extended and evaluated for specific questions.

Didactic focus: Extend and analyze data
Typical challenge: Combination of calculation and data access

Create temperature diagram

The analyzed data is visualized in a line diagram to make temporal developments visible. This establishes the connection between numerical analysis and graphical representation.

Didactic focus: Visualize time series
Typical challenge: Assigning data to diagram axes

Analyze results display

Finally, the most important results are structured and summarized for output, promoting reflection on the analysis and supporting data interpretation.

Didactic focus: Result certification and interpretation
Typical challenge: Selecting relevant information

Work assignment (excerpt)

1. Load CSV file and create first data overview
2. Calculate average temperature values
3. Determine maximum temperatures per month and identify extreme value
4. Graphically represent temperature trends
5. Display analysis results in a structured manner

Example (excerpt)

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df = pd.read_csv("klima.csv")
mittelwerte = df[["berlin","hamburg","muenchen"]].mean()
df["max_temp"] = df[["berlin","hamburg","muenchen"]].max(axis=1)
waermster_monat = df.loc[df["max_temp"].idxmax()]

This fragment shows the central connection between data structure, analysis, and evaluation.

Hints for classroom practice

The module can be fully processed in a double lesson with clear intermediate goals
Differenzierung can occur through additional cities or extended evaluations
Results should be reflected together by diagrams and console outputs
Extensions are possible through additional statistical key figures or markings in the diagram
Connects to topics such as statistics, data analysis, or environmental and climate data

This module connects data analysis and visualization, enabling a step-by-step, traceable evaluation of real datasets.

The clear structure of tasks supports a systematic implementation in class and facilitates result documentation. Differentiation is well possible through extensions.

Request a demo access and test the module in your own teaching context.