1. Basic DataFrame Properties 🛠️

Understand your DataFrame’s structure at a glance.

• df.index → Returns the row index labels of the DataFrame.
• df.columns → Returns the column labels as an Index.
• df.dtypes → Returns a Series with each column’s data type.
• df.axes → Returns a list with the row axis and column axis.
• df.shape → Returns a tuple (rows, columns) representing dimensions.
• df.ndim → Returns the number of dimensions (always 2 for DataFrame).
• df.size → Returns total number of elements (rows × columns).
• df.memory_usage() → Shows memory usage for each column plus index.
• df.empty → Returns True if DataFrame has no elements.
• df.attrs → Dictionary for storing custom user metadata.
• df.info() → Prints concise summary: index dtype, columns, non-null counts.

2. Data Type & Object Management 🛠️

Check, infer, or change your DataFrame’s column data types.

• df.astype() → Convert the dtype of one or more columns to a specified type.
• df.convert_dtypes() → Convert columns to best possible dtypes automatically.
• df.infer_objects() → Infer better dtypes for object columns.
• df.copy() → Create a deep or shallow copy to avoid modifying original data.
• df.set_flags() → Set internal metadata flags.
• df.flags → Inspect DataFrame’s flags (rarely used).
• df.attrs → Same as above; user-defined metadata for extra context.

3. Label-Based & Positional Access 🛠️

Access cells, rows, or slices by label or position.

• df.at[row_label, col_label] → Fast label-based scalar access (single cell).
• df.iat[row_index, col_index] → Fast position-based scalar access.
• df.loc[] → Select rows/columns by label; supports slices, conditions, or lists.
• df.iloc[] → Select rows/columns by integer position.

Key: at/iat → single cell (fast). loc/iloc → slices or multiple rows/columns.

4. Iteration & Basic Loops 🛠️

Loop through columns or rows.

• df.__iter__ → Dunder method for iterating over columns (not used directly).
• df.items() → Iterate over (column_name, Series) pairs.
• df.keys() → Alias for df.columns; returns column labels.
• df.iterrows() → Iterate over (index, row Series) pairs; convenient but slower.
• df.itertuples() → Iterate rows as namedtuples; faster than iterrows.

5. Quick Inspection & Conversion 🛠️

Peek at data or convert to NumPy.

• df.head(n) → Return first n rows (default 5).
• df.tail(n) → Return last n rows.
• df.values → Return DataFrame values as a NumPy array (legacy).
• df.to_numpy() → Preferred way to convert DataFrame to NumPy array.

6. Math, Binary Operations & Comparison 🛠️

Element-wise math, matrix dot products, and value-wise comparison.

Arithmetic

• df.add() or df.__add__ → Add element-wise; supports fill_value.
• df.sub() or df.__sub__ → Subtract element-wise.
• df.mul() or df.__mul__ → Multiply element-wise.
• df.div() or df.truediv() → Divide element-wise (true division).
• df.floordiv() → Floor division.
• df.mod() → Modulo.
• df.pow() → Exponentiate.
• df.dot() → Matrix multiplication.
• df.radd(), df.rsub(), df.rmul(), df.rdiv(), df.rtruediv(), df.rfloordiv(), df.rmod(), df.rpow() → Reverse operations.

Comparison

• df.lt() → Element-wise less than.
• df.gt() → Greater than.
• df.le() → Less than or equal.
• df.ge() → Greater than or equal.
• df.eq() → Equal to.
• df.ne() → Not equal to.

Combine

• df.combine(other, func) → Combine two DataFrames element-wise using a function.
• df.combine_first(other) → Fill missing values with other.

7. Function Application

Apply functions row-wise, column-wise, element-wise, or via a clean pipe.

• df.apply(func, axis=0) → Apply function along an axis (0 = columns, 1 = rows).
• df.applymap(func) → Apply function element-wise.
• df.agg() or df.aggregate() → Aggregate using one or more operations.
• df.transform() → Transform rows/columns; shape is preserved.
• df.pipe(func) → Pipe DataFrame through a custom function.

8. Aggregation & Descriptive Statistics

Describe or summarize your data.

• df.sum() → Sum of values.
• df.mean() → Mean value.
• df.std() → Standard deviation.
• df.var() → Variance.
• df.count() → Count non-NA cells.
• df.min() → Minimum value.
• df.max() → Maximum value.
• df.median() → Median value.
• df.mode() → Mode(s).
• df.prod() or df.product() → Product of values.
• df.cumsum() → Cumulative sum.
• df.cumprod() → Cumulative product.
• df.cummax() → Cumulative max.
• df.cummin() → Cumulative min.
• df.rank() → Rank values.
• df.quantile() → Return value at specified quantile.
• df.pct_change() → Percent change over previous row.
• df.kurt() or df.kurtosis() → Kurtosis.
• df.skew() → Skewness.
• df.sem() → Standard error of mean.
• df.describe() → Generate descriptive statistics summary.
• df.corr() → Correlation matrix.
• df.cov() → Covariance matrix.
• df.corrwith(other) → Correlation with another DataFrame.
• df.nunique() → Count distinct elements.
• df.value_counts() → Count unique value frequencies.

9. Filtering & Conditional

Filter rows conditionally.

• df.isin(values) → Check if each element is in values.
• df.where(cond) → Replace where condition is False.
• df.mask(cond) → Replace where condition is True.
• df.query(expr) → Query DataFrame with string expression.

10. Reshaping & Pivoting

Switch between wide and long forms.

• df.melt() → Unpivot columns to rows (wide → long).
• df.pivot() → Reshape long to wide; unique index/column pairs.
• df.pivot_table() → Spreadsheet-style pivot with aggregation.
• df.stack() → Pivot columns into index (wide → long).
• df.unstack() → Pivot index levels into columns (long → wide).
• df.explode() → Transform list-like values to separate rows.

11. Missing Data & Cleaning

Detect, drop, or fill NaNs and duplicates.

• df.isna() or df.isnull() → Detect missing values.
• df.notna() or df.notnull() → Detect non-missing.
• df.fillna(value) → Fill NaNs with a value.
• df.dropna() → Drop rows/columns with NaNs.
• df.ffill() or df.pad() → Forward-fill missing.
• df.bfill() or df.backfill() → Backward-fill.
• df.duplicated() → Mark duplicate rows.
• df.drop_duplicates() → Drop duplicate rows.

12. Merge, Join & Combine

Combine multiple DataFrames.

• df.merge() → SQL-style joins.
• df.join() → Join columns using index or key.
• df.update() → Update in place using non-NA values from another DataFrame.

13. Export & IO

Save DataFrame or convert to Python objects.

• df.to_csv() → Save as CSV.
• df.to_excel() → Save as Excel file.
• df.to_json() → Save as JSON.
• df.to_pickle() → Serialize as pickle.
• df.to_sql() → Write to SQL database.
• df.to_dict() → Convert to dictionary.
• df.to_numpy() → Convert to NumPy array.

Aliases & Dunder Reminders

• agg = aggregate
• kurt = kurtosis
• prod = product
• ffill = pad
• bfill = backfill
• isna = isnull
• notna = notnull
• div = truediv
• __add__ etc. = dunder methods; use add(), sub(), etc. instead.