Pandas Fundamentals in 40 Minutes

Pandas is the Python library that made data science what it is today. Ten verbs get you through 90 percent of day-to-day data work.

45 min · Reviewed 2026

Pandas Is the Table API

Pandas was created in 2008 by Wes McKinney at a hedge fund. Today it is the default Python library for tabular data, downloaded over 100 million times per month. Its two main types are Series (a single column) and DataFrame (a table).

Ten verbs you will use constantly

import pandas as pd

# 1. Load
df = pd.read_csv('data.csv')

# 2. Peek
df.head()
df.info()
df.describe()

# 3. Select columns
df['age']                      # one column (Series)
df[['age', 'income']]          # multiple columns (DataFrame)

# 4. Filter rows
df[df['age'] > 18]
df[(df['age'] > 18) & (df['country'] == 'US')]

# 5. Sort
df.sort_values('income', ascending=False)

# 6. Create columns
df['income_per_age'] = df['income'] / df['age']

# 7. Group and aggregate
df.groupby('country')['income'].mean()
df.groupby(['country', 'gender']).agg({
    'income': ['mean', 'median'],
    'age': 'mean'
})

# 8. Join tables
merged = pd.merge(df, other_df, on='user_id', how='left')

# 9. Pivot
pd.pivot_table(df, index='country', columns='year', values='income')

# 10. Save
df.to_csv('clean.csv', index=False)
df.to_parquet('clean.parquet')The ten most important pandas operations

Indexing: the most confusing part

# .loc uses labels
df.loc[5]                      # row with index label 5
df.loc[df['age'] > 18, 'name'] # name column, filtered rows

# .iloc uses positions
df.iloc[5]                     # 6th row regardless of index label
df.iloc[:10, :3]               # first 10 rows, first 3 cols

# Chained assignment is a trap
# df[df.age > 18]['score'] = 100  # DO NOT DO THIS
df.loc[df.age > 18, 'score'] = 100  # CORRECTCorrect indexing patterns

Common patterns worth memorizing

# Top N per group
top3 = df.groupby('country').apply(
    lambda g: g.nlargest(3, 'income')
).reset_index(drop=True)

# Rolling stats
df['7d_avg'] = df['sales'].rolling(window=7).mean()

# Replace based on mapping
df['country'] = df['country'].replace({'USA': 'US', 'U.S.A.': 'US'})

# One-hot encoding
df_encoded = pd.get_dummies(df, columns=['color'])

# Handle dates
df['date'] = pd.to_datetime(df['date'])
df['day_of_week'] = df['date'].dt.day_name()Patterns you will use every week

The big idea: pandas rewards the ten verbs you use 90 percent of the time. Master those before chasing fancier features, and the other 10 percent will come naturally when you need it.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-data-pandas-fundamentals

What pandas object type represents a single column of data?
1. Series
2. Matrix
3. Array
4. DataFrame
Who created the pandas library and in what year?
1. Wes McKinney in 2008
2. Guido van Rossum in 2008
3. Wes McKinney in 2015
4. Guido van Rossum in 1991
What does the SettingWithCopy warning typically indicate?
1. Your computer is running out of memory
2. You are modifying a slice that might be a copy of the original data
3. The DataFrame contains corrupted data
4. Your pandas installation is outdated
How can you avoid the SettingWithCopy warning when modifying DataFrame data?
1. Use .iloc[] with chained indexing
2. Use .ix[] for all selections
3. Restart Python after each operation
4. Use .loc[] with a single operation
In what programming language is the Polars library written?
1. Rust
2. Java
3. Python
4. C++
Compared to pandas, Polars typically runs how much faster on large datasets (over 1 GB)?
1. 50 to 100 times faster
2. 200 to 500 times faster
3. 5 to 20 times faster
4. 1 to 2 times faster
What concept does Polars make more explicit than pandas through its API design?
1. Type hinting
2. Object-oriented programming
3. Functional programming
4. Lazy versus eager evaluation
Which pandas method is used to group data by one or more columns for aggregation?
1. partition
2. aggregate
3. segment
4. groupby
According to the teaching approach in this material, what percentage of data work can be accomplished with ten core verbs?
1. 90 percent
2. 75 percent
3. 50 percent
4. 25 percent
What is the primary pandas data structure for representing tabular data with rows and columns?
1. Dataset
2. DataFrame
3. Series
4. Panel
What aspect of pandas is described as the most confusing part for users?
1. Reading files
2. Plotting
3. Indexing
4. Exporting data
How many times per month is pandas downloaded according to the material?
1. Over 100 million times
2. Fewer than 100,000 times
3. Around 10 million times
4. About 1 million times
Which library was created more recently and is often used as a high-performance alternative to pandas?
1. Polars
2. SciPy
3. Matplotlib
4. NumPy
What is the recommended learning strategy before exploring advanced pandas features?
1. Memorize all function parameters
2. Master the ten verbs used 90% of the time
3. Study source code compilation
4. Learn three other libraries first
What does the .loc[] accessor in pandas use for selection?
1. Regular expressions
2. Integer position indexing
3. Label-based indexing
4. Boolean masks only

← Back to interactive lesson

Tendril · Creators · AI Foundations

Pandas Fundamentals in 40 Minutes

Pandas is the Python library that made data science what it is today. Ten verbs get you through 90 percent of day-to-day data work.

45 min · Reviewed 2026

Pandas Is the Table API

Ten verbs you will use constantly

import pandas as pd

# 1. Load
df = pd.read_csv('data.csv')

# 2. Peek
df.head()
df.info()
df.describe()

# 3. Select columns
df['age']                      # one column (Series)
df[['age', 'income']]          # multiple columns (DataFrame)

# 4. Filter rows
df[df['age'] > 18]
df[(df['age'] > 18) & (df['country'] == 'US')]

# 5. Sort
df.sort_values('income', ascending=False)

# 6. Create columns
df['income_per_age'] = df['income'] / df['age']

# 7. Group and aggregate
df.groupby('country')['income'].mean()
df.groupby(['country', 'gender']).agg({
    'income': ['mean', 'median'],
    'age': 'mean'
})

# 8. Join tables
merged = pd.merge(df, other_df, on='user_id', how='left')

# 9. Pivot
pd.pivot_table(df, index='country', columns='year', values='income')

# 10. Save
df.to_csv('clean.csv', index=False)
df.to_parquet('clean.parquet')The ten most important pandas operations

Indexing: the most confusing part

# .loc uses labels
df.loc[5]                      # row with index label 5
df.loc[df['age'] > 18, 'name'] # name column, filtered rows

# .iloc uses positions
df.iloc[5]                     # 6th row regardless of index label
df.iloc[:10, :3]               # first 10 rows, first 3 cols

# Chained assignment is a trap
# df[df.age > 18]['score'] = 100  # DO NOT DO THIS
df.loc[df.age > 18, 'score'] = 100  # CORRECTCorrect indexing patterns

Common patterns worth memorizing

# Top N per group
top3 = df.groupby('country').apply(
    lambda g: g.nlargest(3, 'income')
).reset_index(drop=True)

# Rolling stats
df['7d_avg'] = df['sales'].rolling(window=7).mean()

# Replace based on mapping
df['country'] = df['country'].replace({'USA': 'US', 'U.S.A.': 'US'})

# One-hot encoding
df_encoded = pd.get_dummies(df, columns=['color'])

# Handle dates
df['date'] = pd.to_datetime(df['date'])
df['day_of_week'] = df['date'].dt.day_name()Patterns you will use every week

The big idea: pandas rewards the ten verbs you use 90 percent of the time. Master those before chasing fancier features, and the other 10 percent will come naturally when you need it.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-data-pandas-fundamentals

What pandas object type represents a single column of data?
1. Series
2. Matrix
3. Array
4. DataFrame
Who created the pandas library and in what year?
1. Wes McKinney in 2008
2. Guido van Rossum in 2008
3. Wes McKinney in 2015
4. Guido van Rossum in 1991
What does the SettingWithCopy warning typically indicate?
1. Your computer is running out of memory
2. You are modifying a slice that might be a copy of the original data
3. The DataFrame contains corrupted data
4. Your pandas installation is outdated
How can you avoid the SettingWithCopy warning when modifying DataFrame data?
1. Use .iloc[] with chained indexing
2. Use .ix[] for all selections
3. Restart Python after each operation
4. Use .loc[] with a single operation
In what programming language is the Polars library written?
1. Rust
2. Java
3. Python
4. C++
Compared to pandas, Polars typically runs how much faster on large datasets (over 1 GB)?
1. 50 to 100 times faster
2. 200 to 500 times faster
3. 5 to 20 times faster
4. 1 to 2 times faster
What concept does Polars make more explicit than pandas through its API design?
1. Type hinting
2. Object-oriented programming
3. Functional programming
4. Lazy versus eager evaluation
Which pandas method is used to group data by one or more columns for aggregation?
1. partition
2. aggregate
3. segment
4. groupby
According to the teaching approach in this material, what percentage of data work can be accomplished with ten core verbs?
1. 90 percent
2. 75 percent
3. 50 percent
4. 25 percent
What is the primary pandas data structure for representing tabular data with rows and columns?
1. Dataset
2. DataFrame
3. Series
4. Panel
What aspect of pandas is described as the most confusing part for users?
1. Reading files
2. Plotting
3. Indexing
4. Exporting data
How many times per month is pandas downloaded according to the material?
1. Over 100 million times
2. Fewer than 100,000 times
3. Around 10 million times
4. About 1 million times
Which library was created more recently and is often used as a high-performance alternative to pandas?
1. Polars
2. SciPy
3. Matplotlib
4. NumPy
What is the recommended learning strategy before exploring advanced pandas features?
1. Memorize all function parameters
2. Master the ten verbs used 90% of the time
3. Study source code compilation
4. Learn three other libraries first
What does the .loc[] accessor in pandas use for selection?
1. Regular expressions
2. Integer position indexing
3. Label-based indexing
4. Boolean masks only

← Back to interactive lesson