Lære Exploring Data with Pandas | Data-Driven Decision Making

When you need to make sense of business data, the pandas library in Python is one of your most valuable tools. Pandas makes it easy to organize, explore, and analyze tabular data—like sales records, customer lists, or inventory tables—using a familiar spreadsheet-like structure. With pandas, you can quickly spot trends, summarize key numbers, and prepare your data for deeper analysis, all using straightforward Python code.


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import pandas as pd

# Create a DataFrame with sales data
data = {
    "product": ["Widget", "Gadget", "Doohickey", "Widget", "Gadget"],
    "units_sold": [10, 15, 7, 12, 9],
    "revenue": [200, 450, 140, 240, 270]
}

df = pd.DataFrame(data)
print(df)

A pandas DataFrame is a two-dimensional table with labeled columns (like "product", "units_sold", and "revenue") and rows, similar to a spreadsheet. Each column can hold different types of data, such as numbers or strings. You can use head() to view the first few rows of your data, or describe() to get quick summary statistics for each numeric column. Selecting a column is as simple as using its label in square brackets, for example df["revenue"] to see all revenue values.


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# Filter for products with revenue greater than 200
high_revenue = df[df["revenue"] > 200]
print(high_revenue)

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Sektion 2. Kapitel 2

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Suggested prompts:

How can I filter for products with units_sold greater than a certain value?

Can you show me how to get the total revenue for each product?

What does the describe() function output look like for this DataFrame?

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              1234567891011
            
import pandas as pd

# Create a DataFrame with sales data
data = {
    "product": ["Widget", "Gadget", "Doohickey", "Widget", "Gadget"],
    "units_sold": [10, 15, 7, 12, 9],
    "revenue": [200, 450, 140, 240, 270]
}

df = pd.DataFrame(data)
print(df)


              123
            
# Filter for products with revenue greater than 200
high_revenue = df[df["revenue"] > 200]
print(high_revenue)

Var alt klart?

Tak for dine kommentarer!

Sektion 2. Kapitel 2

Exploring Data with Pandas

1. What is a pandas DataFrame?

2. How can you select a specific column from a DataFrame?

3. Why is pandas useful for startup founders?

Exploring Data with Pandas

1. What is a pandas DataFrame?

2. How can you select a specific column from a DataFrame?

3. Why is pandas useful for startup founders?