title_wip

**Examples:**

```
ds = pd.read_csv("data.csv")

# Basic statistics
print(ds['salary'].mean())
print(ds['age'].std())
print(ds.describe())

# Group statistics
print(ds.groupby('department')['salary'].mean())
print(ds.groupby('city').agg({'salary': ['mean', 'std'], 'age': 'count'}))
```

---

## Data Manipulation[​](#manipulation "Direct link to Data Manipulation")

|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method Description|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `drop()` Drop rows/columns|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `drop_duplicates()` Remove duplicates|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `duplicated()` Mark duplicates|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `dropna()` Remove missing values|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `fillna()` Fill missing values|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `ffill()` Forward fill|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `bfill()` Backward fill|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `interpolate()` Interpolate values|  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `replace()` Replace values|  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `rename()` Rename columns/index|  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `rename_axis()` Rename axis|  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | | `assign()` Add new columns|  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | | `astype()` Convert types|  |  |  |  | | --- | --- | --- | --- | | `convert_dtypes()` Infer types|  |  | | --- | --- | | `copy()` Copy DataFrame | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |

**Examples:**

```
ds = pd.read_csv("data.csv")

# Drop operations
result = ds.drop(columns=['unused_col'])
result = ds.drop_duplicates(subset=['user_id'])
result = ds.dropna(subset=['email'])

# Fill operations
result = ds.fillna(0)
result = ds.fillna({'age': 0, 'name': 'Unknown'})

# Transform operations
result = ds.rename(columns={'old_name': 'new_name'})
result = ds.assign(
    full_name=lambda x: x['first_name'] + ' ' + x['last_name'],
    age_group=lambda x: pd.cut(x['age'], bins=[0, 25, 50, 100])
)
```

---

## Sorting and Ranking[​](#sorting "Direct link to Sorting and Ranking")

|  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method Description|  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | | `sort_values()` Sort by values|  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | | `sort_index()` Sort by index|  |  |  |  | | --- | --- | --- | --- | | `nlargest()` N largest values|  |  | | --- | --- | | `nsmallest()` N smallest values | | | | | | | | | |

**Examples:**

```
# Sort by single column
result = ds.sort_values('salary', ascending=False)

# Sort by multiple columns
result = ds.sort_values(['department', 'salary'], ascending=[True, False])

# Get top/bottom N
result = ds.nlargest(10, 'salary')
result = ds.nsmallest(5, 'age')
```

---

## Reshaping[​](#reshaping "Direct link to Reshaping")

|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method Description|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `pivot()` Pivot table|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `pivot_table()` Pivot with aggregation|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `melt()` Unpivot|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `stack()` Stack columns to index|  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `unstack()` Unstack index to columns|  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `transpose()` / `T` Transpose|  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `explode()` Explode lists to rows|  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | | `squeeze()` Reduce dimensions|  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | | `droplevel()` Drop index level|  |  |  |  | | --- | --- | --- | --- | | `swaplevel()` Swap index levels|  |  | | --- | --- | | `reorder_levels()` Reorder levels | | | | | | | | | | | | | | | | | | | | | | | |

**Examples:**

```
# Pivot table
result = ds.pivot_table(
    values='amount',
    index='region',
    columns='product',
    aggfunc='sum'
)

# Melt (unpivot)
result = ds.melt(
    id_vars=['name'],
    value_vars=['score1', 'score2', 'score3'],
    var_name='test',
    value_name='score'
)

# Explode arrays
result = ds.explode('tags')
```

---

## Combining / Joining[​](#combining "Direct link to Combining / Joining")