Python Pandas DataFrame
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Editing Columns
Adding a new column is as simple as assigning to a new key, like df[total] = df[price] * df[qty], which computes element-wise across every row at once. Modifying an existing column works the same way, and df.drop(columns=[...]) removes columns you no longer need.
Example: Editing Columns
import pandas as pd
df = pd.DataFrame({"price": [10, 20], "qty": [2, 3]})
df["total"] = df["price"] * df["qty"]
print(df)Handling Missing Data
Real-world data is rarely complete: df.isna() flags missing values, df.dropna() removes rows or columns containing them, and df.fillna(value) replaces them with a specified value or a computed statistic like the column mean. Choosing between dropping and filling depends on whether the missing data is rare enough to discard safely.
Example: Handling Missing Data
import pandas as pd
import numpy as np
df = pd.DataFrame({"value": [1, np.nan, 3]})
print(df.fillna(0))Sorting DataFrames
df.sort_values(column) reorders the DataFrame's rows based on the values in one or more specified columns, ascending by default, with a ascending=False option for descending order. This is the DataFrame equivalent of Python's sorted(), but operating over full labeled rows instead of standalone values.
Example: Sorting DataFrames
import pandas as pd
df = pd.DataFrame({"score": [70, 90, 50]})
print(df.sort_values("score"))Grouping Data
df.groupby(column) splits the DataFrame into groups sharing the same value in that column, and chaining an aggregation like .mean() or .sum() afterward computes that statistic separately within each group. This split-apply-combine pattern is the core tool for summarizing data by category.
Example: Grouping Data
import pandas as pd
df = pd.DataFrame({"team": ["A", "A", "B"], "score": [10, 20, 30]})
print(df.groupby("team").mean())Merging and Joining
pd.concat() stacks multiple DataFrames together (by rows or columns), while pd.merge() joins them based on matching values in a shared key column, similar to a SQL join. Merging is essential whenever related data is split across multiple tables that need to be combined for analysis.
Example: Merging and Joining
import pandas as pd
a = pd.DataFrame({"id": [1, 2], "name": ["Alex", "Sam"]})
b = pd.DataFrame({"id": [1, 2], "score": [90, 80]})
print(pd.merge(a, b, on="id"))Chapter Quiz — Complete all 14 topics to unlock
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- Python NumPy Introduction
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- Python Pandas Introduction
- Python Pandas DataFrame
- Python Matplotlib Basics
- Python Data Visualization
- Python Statistics Module
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- Python requests Module
- Python JSON & APIs
- Python Web Scraping Basics
- Python Flask Introduction
- Python Django Introduction
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