Pandas CSV
Work with CSV files, read and write like a boss.
Example 1: Read a CSV file.
df = pd.read_csv('file.csv')
Example 2: Write a DataFrame to CSV.
df.to_csv('output.csv', index=False)
Handle CSVs like a data wizard.
Pandas JSON
Handle JSON data with ease.
Example 1: Read JSON.
df = pd.read_json('file.json')
Example 2: Write JSON.
df.to_json('output.json', orient='records')
JSON is your gateway to modern data formats.
Pandas Cleaning Data
Clean data faster than your roommate avoids doing dishes.
Example 1: Strip whitespace.
df['Column'] = df['Column'].str.strip()
Example 2: Standardize case.
df['Column'] = df['Column'].str.lower()
Clean data, clean mind.
Pandas Missing Values
Handle missing values like a pro.
Example 1: Fill missing values.
df = pd.DataFrame({'A': [1, None, 3]})
df.fillna(0, inplace=True)
Example 2: Drop rows with missing data.
df.dropna(inplace=True)
Because no one likes a blank space (thanks, Taylor Swift).
Fix your messy data formats.
Example 1: Convert strings to dates.
df['Date'] = pd.to_datetime(df['Date'])
Example 2: Convert strings to numbers.
df['Numeric'] = pd.to_numeric(df['Numeric'], errors='coerce')
Wrong format? Not anymore.
Pandas Handling Wrong Data
Replace invalid values.
Example: Replace placeholders with None.
df['Column'] = df['Column'].replace('?', None)
No more wrong data ruining your day.
Pandas Get Dummies
Convert categories to indicator variables.
Example: Create dummy variables.
dummies = pd.get_dummies(df['Category'])
print(dummies)
Perfect for machine learning models.
Pandas Categorical
Optimize memory usage with categorical data types.
Example: Convert to categorical.
df['Category'] = df['Category'].astype('category')
Save memory, save the planet.
Pandas Datetime
Work with datetime like a pro.
Example 1: Extract the year.
df['Year'] = pd.to_datetime(df['Date']).dt.year
Example 2: Extract the month.
df['Month'] = pd.to_datetime(df['Date']).dt.month
DateTime makes time manipulation a breeze.
Pandas Aggregate Functions
Summarize data efficiently.
Example: Group by and aggregate.
grouped = df.groupby('Category').agg({'Value': 'sum'})
print(grouped)
Aggregations are the secret sauce to insights.
Pandas Group By
Group data for analysis.
Example: Group and calculate mean.
print(df.groupby('Category').mean())
Group by like a data Jedi.
Pandas Filtering
Filter rows based on conditions.
Example: Filter rows.
df[df['Value'] > 10]
Keep what you love, ditch the rest.
Pandas Sort
Sort data like a pro.
Example 1: Sort rows by a column.
df.sort_values(by='Value', ascending=False, inplace=True)
Example 2: Sort by index.
df.sort_index(inplace=True)
Sorting is as easy as pie.
Pandas Correlation
Check correlations between numerical columns.
Example: Compute correlation.
print(df.corr())
Correlation = your shortcut to patterns.
Pandas Plot
Visualize data without leaving Pandas.
Example: Line plot.
df.plot(kind='line')
Example: Bar plot.
df.plot(kind='bar')
Pandas plotting is your gateway to quick insights.
Pandas Histogram
Understand distributions with histograms.
Example 1: Simple histogram.
df['Value'].hist()
Example 2: Multiple histograms.
df[['A', 'B']].hist()
Histograms: because data speaks louder than words.
