Project 2 WorkBook

Tutoring Lab Info

The data science lab is a resource you can use in person, online, and in Slack.

Data Manipulation

1. `replace()` function

Replace Function Explanation

The replace() function is used to substitute specified values in a DataFrame or Series, facilitating data cleaning and transformation by exchanging specific values with desired ones. In the example, old_value denotes the value to be replaced, and new_value signifies the replacement value. The presence of inplace=True ensures that the modification is applied directly to the DataFrame.

Code Snippet

df['column_name'].replace(old_value, new_value, inplace=True)

2. `min()` function

Min Function Explanation

The min() function is employed to determine the minimum value in a DataFrame or Series. It is particularly useful for extracting the smallest element from a numerical dataset.

Code Snippet

min_value = df['column_name'].min()

3. `max()` function

Max Function Explanation

The max() function is utilized to find the maximum value in a DataFrame or Series. This function is handy for retrieving the largest element from a numerical dataset.

Code Snippet

max_value = df['column_name'].max()

4. `groupby()` function

Groupby Function Explanation

The groupby() function is applied to split the data into groups based on some criteria. It is often used in conjunction with aggregate functions like sum() to perform operations on each group independently.

Code Snippet

grouped_data = df.groupby('grouping_column').sum()

5. `sum()` function

Sum Function Explanation

The sum() function is used to calculate the sum of values in a DataFrame or Series. It is commonly employed with the groupby() function to find the total of specific groups in the data.

Code Snippet

sum_result = df['column_name'].sum()

6. `index` attribute

Index Function Explanation

The index attribute is employed to access the index (row labels) of a DataFrame. It is useful for extracting information related to the row labels and their positions.

Code Snippet

index_values = df.index

7. `round()` function

Round Function Explanation

The round() function is utilized to round off numeric values to a specified number of decimal places. It is helpful for controlling the precision of numerical data in a DataFrame.

Code Snippet

rounded_values = df['column_name'].round(decimals=2)

8. Indexing a String

Indexing a String Explanation

To index a string in a DataFrame, the square brackets [] are used along with the column name containing the string data. This allows for accessing and manipulating specific characters or substrings within the string.

Code Snippet

string_value = df['string_column'][index]

9. `to_json()` function

To JSON Function Explanation

The to_json() function is employed to convert a DataFrame to its JSON representation. The orient parameter determines the format of the JSON output, such as ‘split’, ‘records’, ‘index’, etc.

Code Snippet

json_representation = df.to_json(orient='records')

10. `isin()` function

Isin Function Explanation

The isin() function is used to filter data based on a list of values. It returns a boolean Series indicating whether each element in the Series is contained in the specified list.

Code Snippet

filtered_data = df[df['column_name'].isin(['value1', 'value2'])]

11. `all()` function

All Function Explanation

The all() function is employed to check if all elements in a boolean Series are True. It is commonly used in conjunction with conditions to evaluate whether a certain condition holds for all elements.

Code Snippet

all_elements_true = df['boolean_column'].all()

12. `fillna()` function

Fillna Function Explanation

The fillna() function is used to fill missing (NaN) values in a DataFrame with a specified value or using a specified method. It helps in handling missing data by providing a way to replace or impute missing values.

Code Snippet

filled_data = df['column_name'].fillna(value)

13. `mean()` function

Mean Function Explanation

The mean() function calculates the mean (average) of values in a DataFrame or Series. It is useful for obtaining the central tendency of numerical data, providing insights into the data distribution.

Code Snippet

mean_value = df['column_name'].mean()

Getting started with Lets-Plot

Lets-Plot

What makes a chart look good?

Note: The following code snippets use Plotly Express for demonstartion. The course uses Lets-Plot.

Show the code

import plotly.express as px
import pandas as pd
url = "https://github.com/byuidatascience/data4python4ds/raw/master/data-raw/mpg/mpg.csv"
data = pd.read_csv(url)

Plotly Chart Structure

Show the code

fig = px.scatter(data, x='displ', y='hwy')
fig.show()

Size of Chart

Width and Height

Show the code

fig = px.scatter(data, x='displ', y='hwy')
fig.update_layout(width=600, height=600)

fig.show()

Width and Height Explanation

The width and height parameters in the update_layout method are used to set the width and height of the plot in pixels, respectively. In this example, the width is set to 600 pixels, and the height is also set to 600 pixels. Adjust these values according to your desired dimensions for the scatter plot.

Title and Subtitle

Title

Show the code

fig = px.bar(data, x='cty', y='hwy')
fig.update_layout(title_text="Bar Chart Example")
fig.show()

Title Explanation

The title_text parameter in the update_layout method is used to set the title of the plot. In this example, the title is set to “Bar Chart Example”. You can customize the title by changing the value assigned to title_text to better describe the content or purpose of your bar chart.

Title and subtitle - Title w/ Subtitle 1

Show the code

fig = px.bar(data, x='cty', y='hwy')

fig.update_layout(
    title_text="City vs Highway MPG Bar Chart",
    title_font=dict(color="red"),
    title_font_size=18,
    title_y=0.95,
    title_x=0.5
)

fig.add_annotation(
    text="Your Annotation Text",
    font=dict(color="blue"),  # Choose your desired font color
    x=0.5,
    y=0.9,
    showarrow=False
)
fig.show()

Title and subtitle - Title w/ Subtitle 1 Explanation

The title_text parameter in the update_layout method is used to set the main title of the plot, and additional parameters like title_font, title_font_size, title_y, and title_x are used to customize the appearance and position of the title.

The add_annotation method is used to add an annotation or subtitle to the plot. In this example, it adds a blue text annotation with the content “Your Annotation Text” at a specified position (x=0.5, y=0.9) relative to the plot.

Adjust the values of these parameters to customize the appearance and position of the title and annotation according to your preferences.

Title and subtitle - Title w/ Subtitle 2

Show the code

fig = px.bar(data, x='cty', y='hwy')

fig.update_layout(
    title_text="City vs Highway MPG Bar Chart",
    title_font_size=18,
    title_y=0.95,
    title_x=0.5
)

fig.add_annotation(
    text="Your Annotation Text",
    x=0.5,
    y=0.9,
    showarrow=False
)

fig.show()

Title and subtitle - Title w/ Subtitle 2 Explanation

The title_text parameter in the update_layout method is used to set the main title of the plot, and additional parameters like title_font_size, title_y, and title_x are used to customize the appearance and position of the title.

The add_annotation method is used to add an annotation or subtitle to the plot. In this example, it adds a text annotation with the content “Your Annotation Text” at a specified position (x=0.5, y=0.9) relative to the plot.

Adjust the values of these parameters to customize the appearance and position of the title and annotation according to your preferences.

Group Variable

Show the code

fig = px.bar(data, x='manufacturer', y=['cty', 'hwy'], barmode='group')

fig.update_layout(
    xaxis_title="Manufacturer",
    yaxis_title="Mileage",
    title_text="Average City and Highway Mileage by Manufacturer"
)

fig.show()

Group Variable Explanation

The x parameter in the px.bar function is set to ‘manufacturer’, which means the bars will be grouped by the ‘manufacturer’ variable on the x-axis. The y parameter is set to a list [‘cty’, ‘hwy’], indicating that two sets of bars will be plotted for each manufacturer, one for ‘cty’ and another for ‘hwy’.

The barmode='group' parameter ensures that the bars are grouped for each ‘manufacturer’.

The update_layout method is used to set the titles for the x-axis (xaxis_title), y-axis (yaxis_title), and the overall plot (title_text). In this example, the plot represents the average city and highway mileage by manufacturer.

Axis formatting - Axis Scale removing Zero

Show the code

fig = px.bar(data, x='manufacturer', y=['cty', 'hwy'], barmode='group')

fig.update_layout(
    xaxis_title="Manufacturer",
    yaxis_title="Mileage",
    title_text="Average City and Highway Mileage by Manufacturer",
    xaxis=dict(showline=True, showgrid=False),
    yaxis=dict(zeroline=False, showline=True, showgrid=False),
)

fig.show()

Axis formatting - Axis Scale removing Zero Explanation

The xaxis_title and yaxis_title parameters in the update_layout method are used to set the titles for the x-axis and y-axis, respectively.

The xaxis and yaxis dictionaries in the update_layout method provide additional formatting options for the x-axis and y-axis. In this example:

xaxis=dict(showline=True, showgrid=False) ensures that the x-axis has a visible line but no grid lines. yaxis=dict(zeroline=False, showline=True, showgrid=False) ensures that the y-axis has no zero line (zeroline=False), a visible line, and no grid lines. These settings help customize the appearance of the plot by controlling the visibility of axis lines and grid lines.

Axis formatting - Axis Domain Sizing

Show the code

fig = px.bar(data, x='manufacturer', y=['cty', 'hwy'], barmode='group')

fig.update_layout(
    xaxis_title="Manufacturer",
    yaxis_title="Mileage",
    title_text="Average City and Highway Mileage by Manufacturer",
    xaxis=dict(domain=[0.1, 0.9]),  # Adjust the domain as needed
)

fig.show()

Axis formatting - Axis Domain Sizing Explanation

The xaxis_title and yaxis_title parameters in the update_layout method are used to set the titles for the x-axis and y-axis, respectively.

The xaxis dictionary in the update_layout method includes the domain parameter, which is set to [0.1, 0.9]. This parameter controls the size of the x-axis domain, determining the portion of the total width of the plot that the x-axis occupies. In this example, the x-axis is set to span from 10% to 90% of the total width.

Adjust the values of the domain parameter as needed to control the sizing of the x-axis in your plot.

Reference marks - Verticle Reference Line with Color

Show the code

fig = px.bar(data, x='manufacturer', y=['cty', 'hwy'], barmode='group')

fig.update_layout(
    xaxis_title="Manufacturer",
    yaxis_title="Mileage",
    title_text="Average City and Highway Mileage by Manufacturer"
)

fig.add_shape(
    dict(
        type="line",
        x0="Your_X_Value",  # Specify the x-coordinate of the line
        x1="Your_X_Value",  # Specify the x-coordinate of the line
        y0=0,
        y1=1,
        line=dict(color="red"),  # Specify the color of the line
    )
)

fig.show()

Reference marks - Verticle Reference Line with Color Explanation

The add_shape method is used to add a reference line to the plot. In this example, a vertical reference line is added to the x-axis at the specified x-coordinate value.

The type="line" parameter specifies that the added shape is a line. The x0 and x1 parameters are set to “Your_X_Value” to specify the x-coordinates where the line starts and ends. Replace “Your_X_Value” with the actual x-coordinate value where you want the reference line.

The y0 and y1 parameters set the starting and ending points on the y-axis. In this example, they are set to 0 and 1, respectively.

The line dictionary inside the shape specifies the attributes of the line, such as the color. In this case, the line color is set to red. Adjust the values as needed to customize the appearance of the reference line.

Reference marks - Verticle Reference Line with Text

Show the code

fig = px.bar(data, x='manufacturer', y=['cty', 'hwy'], barmode='group')

fig.update_layout(
    xaxis_title="Manufacturer",
    yaxis_title="Mileage",
    title_text="Average City and Highway Mileage by Manufacturer"
)

fig.add_shape(
    dict(
        type="line",
        x0="Your_X_Value",  # Specify the x-coordinate of the line
        x1="Your_X_Value",  # Specify the x-coordinate of the line
        y0=0,
        y1=1,
        line=dict(color="red"),  # Specify the color of the line
    )
)

fig.add_annotation(
    text="Your Text",
    x="Your_X_Value",  # Specify the x-coordinate of the text
    y=500,  # Adjust the y-coordinate of the text as needed
    showarrow=False
)

fig.show()

Reference marks - Verticle Reference Line with Text Explanation

The add_shape method is used to add a reference line to the plot. In this example, a vertical reference line is added to the x-axis at the specified x-coordinate value.

The y0 and y1 parameters set the starting and ending points on the y-axis. In this example, they are set to 0 and 1, respectively.

The line dictionary inside the shape specifies the attributes of the line, such as the color. In this case, the line color is set to red.

The add_annotation method is used to add text annotation to the plot. The text parameter is set to “Your Text”, and the x and y parameters specify the coordinates where the text will be placed. Adjust the values of these parameters as needed to customize the appearance of the reference line and text.

Project 2 WorkBook

Tutoring Lab Info

Data Manipulation

1. replace() function

2. min() function

3. max() function

4. groupby() function

5. sum() function

6. index attribute

7. round() function

8. Indexing a String

9. to_json() function

10. isin() function

11. all() function

12. fillna() function

13. mean() function

Getting started with Lets-Plot

Plotly Chart Structure

Size of Chart

Title and Subtitle

1. `replace()` function

2. `min()` function

3. `max()` function

4. `groupby()` function

5. `sum()` function

6. `index` attribute

7. `round()` function

9. `to_json()` function

10. `isin()` function

11. `all()` function

12. `fillna()` function

13. `mean()` function