mis315 final notes

# OOP

## Class Definition

```python
class ClassName:
    def __init__(self):         # Constructor (like JS `constructor`)
        self.attribute = value  # Instance variable

    def method_name(self):      # Method (uses `self` like `this` in JS)
        # code block
        pass
```

- self: Explicit reference to the instance (`this` in JS)  
- \_\_init__: Constructor method, runs when you create an object 

## Create and Use Object Instance

```python
obj = ClassName()  
obj.method_name()
```


## Examples

```python
import pandas as pd  

class PropertySales:  
    def __init__(self):  
        self.sum = 0  

    def calculate_total_sale_price(self):  
        df = pd.read_excel('Properties.xlsx')  # reads the spreadsheet  
        self.sum = df['Sale Price'].sum()  

    def get_sum(self):  
        return self.sum  

property_sales = PropertySales()  
property_sales.calculate_total_sale_price()  
total = property_sales.get_sum()  

print(f"The total sale price is: {total}")  
```

```python
# prop.py
class Prop:
    def __init__(self):
        self.__x = None  # Initialize __x properly

    def read_data(self):
        import pandas as pd
        pr = pd.read_excel('Properties.xlsx')
        print(pr)
        self.__x = pr['Sale Price'].sum()  # Store just the sum

    def get_answer(self):
        return self.__x
```

```python
# main.py
import prop

my_prop = prop.Prop()

# You need to call the `read_data` method first to load the Excel and compute the sum
my_prop.read_data()

# Then print the answer
print(my_prop.get_answer())
```

- Every method must include `self` as the first parameter  
- Use `:` after `def`, `class`, `if`, `for`, etc.  
- Indentation matters — use consistent 4-space indents  
- Class names should be in CamelCase by convention  

### What to do in exam
1. Upload the `.xlsx` file to session storage  
2. Upload your class definition `.py` file  
3. Instantiate the class with `obj = ClassName()`  
4. Call methods like `obj.method_name()`  
5. Output results with `print()`

# DataFrame (Pandas / Numpy / Seaborn)

## Libraries

```python
import pandas as pd  
import numpy as np  
import seaborn as sns  
import matplotlib.pyplot as plt
```

## Read Excel File

```python
df = pd.read_excel('filename.xlsx')  
Example:  
df = pd.read_excel('Dogs.xlsx')  
df = pd.read_excel('Properties.xlsx')
```

## Exploring DataFrame

```python
pd.set_option('display.max_columns', None)   # Show all columns  
pd.set_option('display.max_rows', None)      # Show all rows  
pd.reset_option('display.max_columns')       # Reset columns view  
pd.reset_option('display.max_rows')          # Reset rows view  

df = pd.DataFrame(data)                      # Re-wrap Excel into DataFrame  

df.describe()                                # Summary stats (mean, min, max, etc.)  
df.shape                                     # (rows, columns)  
df.info()                                    # Data types & non-null counts  
df.head()                                    # First 5 rows  
df.index                                     # Row index
```

## Sorting & Filtering

```python
df.sort_values('List Price')                          # Ascending  
df.sort_values('List Price', ascending=False)         # Descending  

df[df['List Price'] > 450000]                         # Filter rows  

top5 = df.sort_values(by='Weight_KG', ascending=False).head(5)  
# Sort by Weight and select top 5 dogs  
```

## Plotting with Seaborn

```python
plt.figure(figsize=(12, 6))  
sns.barplot(x='Name', y='Weight_KG', data=top5, palette='bright')  
plt.tight_layout()  
plt.show()
```

## Examples

```python
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_excel('Dogs.xlsx')
top5 = df.sort_values(by='Weight_KG', ascending=False).head(5)

plt.figure(figsize=(12, 6))
sns.barplot(x='Name', y='Weight_KG', data=top5, palette='bright')
plt.tight_layout()
plt.show()
```

```python
import pandas as pd
import numpy as np

# read excel file
df = pd.read_excel('Dogs.xlsx')

# describe
print("Description of dataframe")
print(df.describe())

# list with weight over 25 KG
print("\nList with weight over 25 KG")
print(df[df['Weight_KG'] > 25])

# shape
print("\nShape of the dataframe")
print(df.shape)

# info
print("\nInfo of the dataframe")
print(df.info())

# head
print("\nHead of the dataframe")
print(df.head())

# index
print("\nIndex of the dataframe")
print(df.index)

# sort ascending by weight
print("\nSort ascending by weight")
print(df.sort_values(by='Weight_KG', ascending=True))

# sort descending by weight
print("\nSort descending by weight")
print(df.sort_values(by='Weight_KG', ascending=False)) 
```

### What to do in exam

1. You’ll be given a `.xlsx` spreadsheet in the exam folder  
2. Upload it to session storage  
3. Read it into a DataFrame using `pd.read_excel()`  
4. Perform analysis: sorting, filtering, describing, shape, info  
5. Plot a bar chart using seaborn if asked

# Plotly Express (w/ Gapminder)

## Libraries

```python
import pandas as pd  
import numpy as np  
import plotly.express as px
```

## Read and Filter DataFrame

```python
# Read Excel  
df = pd.read_excel('Properties.xlsx')  

# Filter for sold properties only  
sold_df = df[df['Sold'] == True]  

This filters rows where the "Sold" column is `True`. You can also use `.query("Sold == True")` for the same effect.
```

## Plotly Scatter Plot

```python
fig = px.scatter(  
  sold_df,  
  x='Date Sold',  
  y='List Price',  
  size='Square Feet',  
  color='Subdivision ID',  
  title='Sold Properties'  
)  

fig.show()
```
- `x`: sets the x-axis — here it's `Date Sold`  
- `y`: sets the y-axis — here it's `List Price`  
- `size`: adjusts dot sizes based on square footage  
- `color`: groups points by subdivision for visual clarity  
- `title`: chart title  
- `fig.show()`: renders the chart in notebook/browser

## Gapminer Pie Chart

```python
gap = px.data.gapminder()  
gap2007 = gap.query("year == 2007")  

fig = px.pie(  
  gap2007,  
  values='pop',  
  names='continent',  
  title='Population Share by Continent (2007)'  
)  

fig.show()
```

```python
df = px.data.gapminder().query("year == 1982").query("continent == 'Europe'")  
df.loc[df['pop'] < 5e7, 'country'] = 'Other countries'  # Group small populations together  
fig = px.pie(df, values='pop', names='country', title='Population of European continent')  
fig.show()
```

```python
df = px.data.gapminder()  
fig = px.scatter_geo(  
    df.query("year == 2007"),  
    locations="iso_alpha",  
    color="continent",  
    hover_name="country",  
    size="pop",  
    projection="natural earth"  
)  
fig.show()
```

### What to do in exam
- Use `px.data.gapminder()` OR load a spreadsheet with `pd.read_excel()`  
- Filter using `.query()` or boolean indexing  
- Use `px.pie()`, `px.scatter()`, or `px.scatter_geo()`  
- Assign correct arguments: `x`, `y`, `size`, `color`, `names`, `locations`, etc.  
- Call `fig.show()` to display the chart