Merge branch 'main' into feat(docs)/Update-Python-Hash

content/plotly/concepts/express/terms/funnel/funnel.md

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+---
+Title: '.funnel()'
+Description: 'Generates a funnel chart that visualizes the reduction of data in progressive stages.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Data'
+  - 'Graphs'
+  - 'Libraries'
+  - 'Methods'
+  - 'Plotly'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+The **`.funnel()`** method in Plotly Express creates a funnel chart, which visualizes the progressive reduction of data as it moves through sequential stages. The chart is composed of stacked horizontal bars, with each bar's length representing a value at a specific stage. This helps highlight changes, bottlenecks, or drop-offs in the process.
+
+## Syntax
+
+```pseudo
+plotly.express.funnel(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, ...)
+```
+
+- `data_frame`: The dataset (typically a [Pandas DataFrame](https://www.codecademy.com/resources/docs/pandas/dataframe)) to be plotted. If this is not provided, Plotly Express will construct a DataFrame using the other arguments.
+- `x`: The column in the DataFrame that specifies the values to determine the length of the bars, plotted along the x-axis.
+- `y`: The column in the DataFrame that represents the stages of the funnel, plotted along the y-axis.
+- `color`: The column in the DataFrame that assigns colors to the bars of the funnel.
+- `facet_row`: Splits the funnel chart into vertically-stacked subplots based on a specified column from the DataFrame.
+- `facet_col`: Splits the funnel chart into horizontally-arranged subplots based on a specified column from the DataFrame.
+
+> **Note:** The ellipsis (...) indicates there can be additional optional parameters beyond those listed here.
+
+## Example 1
+
+The example below generates a funnel chart representing the job search process for an applicant:
+
+```py
+import plotly.express as px
+import pandas as pd
+
+# Create sample dictionary
+data = {
+    'Stage': ['Applications Sent', 'Phone Interview', 'Technical Interview', 'Onsite Interview', 'Offers Received', 'Offers Accepted'],
+    'Job Applications': [500, 348, 92, 56, 10, 1]
+}
+
+# Convert the dictionary into a DataFrame
+df = pd.DataFrame(data)
+
+# Create the funnel chart with title "Job Search"
+fig = px.funnel(df, x='Job Applications', y='Stage', title='Job Search')
+
+# Show the chart
+fig.show()
+```
+
+The above example produces the following output:
+
+![Funnel Chart Illustrating Job Search](https://raw.githubusercontent.com/Codecademy/docs/main/media/plotly-express-funnel-example1.png)
+
+## Example 2
+
+As a variation of the previous example, this example adds subplots using the `facet_col` parameter to compare two different job applicants side by side:
+
+```py
+import plotly.express as px
+import pandas as pd
+
+# Create sample dictionary
+data = {
+    'Stage': ['Applications Sent', 'Phone Interview', 'Technical Interview', 'Onsite Interview', 'Offers Received', 'Offers Accepted'] * 2,
+    'Job Applications': [500, 348, 92, 56, 10, 1, 500, 329, 290, 225, 167, 1],
+    'Applicants': ['Candidate 1'] * 6 + ['Candidate 2'] * 6
+}
+
+# Convert the dictionary into a DataFrame
+df = pd.DataFrame(data)
+
+# Create the funnel chart with title "Job Search Comparison"
+fig = px.funnel(df, x='Job Applications', y='Stage', facet_col='Applicants', title='Job Search Comparison')
+
+# Show the chart
+fig.show()
+```
+
+The above code will result in the following output:
+
+![Funnel Chart Comparing Two Applicants](https://raw.githubusercontent.com/Codecademy/docs/main/media/plotly-express-funnel-example2.png)

content/pytorch/concepts/tensors/terms/linspace/linspace.md

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+---
+Title: '.linspace()'
+Description: 'Returns a one-dimensional tensor with a specified number of evenly spaced values between the given start and end points.'
+Subjects:
+  - 'Data Science'
+  - 'Machine Learning'
+Tags:
+  - 'AI'
+  - 'Data Types'
+  - 'Deep Learning'
+  - 'Functions'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'py-torch-for-classification'
+---
+
+The **`.linspace()`** function in PyTorch is used to return a one-dimensional tensor with a specified number of evenly spaced values between the given start and end points.
+
+## Syntax
+
+```pseudo
+torch.linspace(start, end, steps)
+```
+
+- `start`: The starting value to be used.
+- `end`: The ending value to be used.
+- `steps`: The number of steps to be taken between the starting and ending values.
+
+This function is particularly useful when there is a need to create a tensor of equally spaced points for plotting graphs or for performing other numerical computations.
+
+## Example
+
+The following example shows how to use the `.linspace()` function in PyTorch:
+
+```py
+import torch
+
+# Create a tensor of 5 equally spaced points between 0 and 1
+x = torch.linspace(0, 1, 5)
+
+print(x)
+```
+
+The code above generates the following output:
+
+```shell
+tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
+```