If you know spreadsheets, you already understand DataFrames.
Direct answer
A pandas DataFrame is a table with labelled rows and columns. Create one from a dictionary or CSV, select columns with df["name"], filter rows with conditions, add columns by assignment and summarise with groupby.
Examples
import pandas as pd
df = pd.DataFrame({
"name": ["Ada", "Ben", "Chi"],
"age": [28, 35, 22],
"city": ["Lagos", "Accra", "Lagos"],
})
df["age"] # one column
df[["name", "city"]] # several columns
df[df["age"] > 25] # filter rows
df.sort_values("age") # sort
df["age_next_year"] = df["age"] + 1
df.groupby("city")["age"].mean() # average age per city
Common tasks
| Task | Code |
|---|---|
| Read CSV | pd.read_csv("file.csv") |
| First rows | df.head() |
| Summary | df.describe() |
| Rename | df.rename(columns={...}) |
Where Teyro fits
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The bottom line
DataFrames are the workhorse of data analysis in Python.
