{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":71549,"databundleVersionId":8561470,"sourceType":"competition"},{"sourceId":181996661,"sourceType":"kernelVersion"}],"dockerImageVersionId":30732,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Importing the pandas library for data manipulation and analysis\nimport pandas as pd\n\n# Importing the pyplot module from matplotlib for plotting graphs\nfrom matplotlib import pyplot as plt\n\n# Importing seaborn for statistical data visualization\nimport seaborn as sns\n\n# Importing the os module for interacting with the operating system\nimport os\n\n# Importing imageio for reading and writing images\nimport imageio\n\n# Importing display module from IPython for displaying images in Jupyter notebooks\nfrom IPython import display\n\n# Importing the ceil function from the math module for ceiling division\nfrom math import ceil\n\n# Importing the os module again (this is redundant and can be removed)\nimport os\n\n# Importing imageio again (this is redundant and can be removed)\nimport imageio\n\n# Importing pydicom for working with DICOM (Digital Imaging and Communications in Medicine) files\nimport pydicom","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-07T16:54:36.982432Z","iopub.execute_input":"2024-06-07T16:54:36.982889Z","iopub.status.idle":"2024-06-07T16:54:38.581492Z","shell.execute_reply.started":"2024-06-07T16:54:36.982850Z","shell.execute_reply":"2024-06-07T16:54:38.580181Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n```python\n# Defining the base path to the directory containing the training images\nbase_path = '/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_images'\n```\nThis line sets the base path to the directory where the training images are stored. This base path is used to construct full file paths for accessing specific images.\n\n```python\n# Assigning the study ID for the current study\nstudy_id = 4003253\n```\nThis line sets a variable `study_id` to 4003253, which identifies a specific study within the dataset. In medical imaging, a study ID typically refers to a group of related images for a particular patient or examination.\n\n```python\n# Creating a list of dictionaries, each containing a series ID and its description\nseries_ids = [\n    {\"series_id\": 2448190387, \"description\": \"Axial T2\"},\n    {\"series_id\": 1054713880, \"description\": \"Sagittal T1\"},\n    {\"series_id\": 702807833, \"description\": \"Sagittal T2\"}\n]\n```\nThis line creates a list of dictionaries where each dictionary contains a `series_id` and a `description`. A series ID refers to a specific sequence or type of medical imaging (e.g., Axial T2, Sagittal T1) within the study. The description provides additional information about the type of imaging.\n\n```python\n# Creating a list of file paths to the specific DICOM files for the given series IDs\ndicom_file_paths = [\n    base_path+\"/4003253/702807833/8.dcm\",    # Path to a DICOM file in the Sagittal T2 series\n    base_path+\"/4003253/1054713880/8.dcm\",   # Path to a DICOM file in the Sagittal T1 series\n    base_path+\"/4003253/2448190387/20.dcm\"   # Path to a DICOM file in the Axial T2 series\n]\n```\nThis line constructs a list of file paths to specific DICOM files within the study. DICOM (Digital Imaging and Communications in Medicine) is a standard format for medical images. Each path combines the `base_path` with the study ID and series ID to locate a specific image file.\n\n```python\n# Reading the CSV file containing the main training data into a pandas DataFrame\ntrain = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train.csv')\n```\nThis line reads a CSV file named `train.csv` into a pandas DataFrame called `train`. This CSV file contains the main training data, which likely includes various attributes and labels for the images.\n\n```python\n# Reading the CSV file containing label coordinates into a pandas DataFrame\nlabels = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_label_coordinates.csv')\n```\nThis line reads another CSV file named `train_label_coordinates.csv` into a pandas DataFrame called `labels`. This file probably contains coordinates for labels, such as bounding boxes or other annotations relevant to the images.\n\n```python\n# Reading the CSV file containing series descriptions into a pandas DataFrame\nseries = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_series_descriptions.csv')\n```\nThis line reads a CSV file named `train_series_descriptions.csv` into a pandas DataFrame called `series`. This file contains descriptions of the different image series included in the study, providing additional context about the imaging data.\n\n```python\n# Displaying the first few rows of the train DataFrame to get an overview of the data\ntrain.head()\n```\nThis line displays the first few rows of the `train` DataFrame. This is a common practice to quickly inspect the structure and content of the data, allowing you to understand what kind of information is available in the training dataset.","metadata":{}},{"cell_type":"code","source":"# Defining the base path to the directory containing the training images\nbase_path = '/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_images'\n\n# Assigning the study ID for the current study\nstudy_id = 4003253\n\n# Creating a list of dictionaries, each containing a series ID and its description\nseries_ids = [\n    {\"series_id\": 2448190387, \"description\": \"Axial T2\"},\n    {\"series_id\": 1054713880, \"description\": \"Sagittal T1\"},\n    {\"series_id\": 702807833, \"description\": \"Sagittal T2\"}\n]\n\n# Creating a list of file paths to the specific DICOM files for the given series IDs\ndicom_file_paths = [\n    base_path+\"/4003253/702807833/8.dcm\",    # Path to a DICOM file in the Sagittal T2 series\n    base_path+\"/4003253/1054713880/8.dcm\",   # Path to a DICOM file in the Sagittal T1 series\n    base_path+\"/4003253/2448190387/20.dcm\"   # Path to a DICOM file in the Axial T2 series\n]\n\n# Reading the CSV file containing the main training data into a pandas DataFrame\ntrain = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train.csv')\n\n# Reading the CSV file containing label coordinates into a pandas DataFrame\nlabels = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_label_coordinates.csv')\n\n# Reading the CSV file containing series descriptions into a pandas DataFrame\nseries = pd.read_csv('/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_series_descriptions.csv')\n\n# Displaying the first few rows of the train DataFrame to get an overview of the data\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:38.584294Z","iopub.execute_input":"2024-06-07T16:54:38.584949Z","iopub.status.idle":"2024-06-07T16:54:38.794849Z","shell.execute_reply.started":"2024-06-07T16:54:38.584905Z","shell.execute_reply":"2024-06-07T16:54:38.793691Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n```python\n# Displaying a concise summary of the DataFrame 'train'\ntrain.info()\n```\n\nWhen you call `train.info()`, pandas provides a concise summary of the DataFrame, including the following details:\n\n1. **Class type**: The type of the DataFrame.\n2. **Number of entries**: The number of rows in the DataFrame.\n3. **Column names**: The names of the columns in the DataFrame.\n4. **Non-null counts**: The number of non-null entries in each column.\n5. **Data types**: The data type of each column (e.g., integer, float, object).\n6. **Memory usage**: The amount of memory used by the DataFrame.\n\nThis is an example of what the output might look like:\n\n```\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 10000 entries, 0 to 9999\nData columns (total 5 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   StudyID      10000 non-null  int64  \n 1   SeriesID     10000 non-null  int64  \n 2   ImageID      10000 non-null  int64  \n 3   Description  10000 non-null  object \n 4   Label        10000 non-null  int64  \ndtypes: int64(4), object(1)\nmemory usage: 390.8 KB\n```\n\n### Explanation:\n\n- **`<class 'pandas.core.frame.DataFrame'>`**: Indicates that the object is a DataFrame.\n- **RangeIndex: 10000 entries, 0 to 9999**: Shows the range of the index, indicating there are 10,000 rows.\n- **Data columns (total 5 columns)**: Indicates there are 5 columns in the DataFrame.\n- **`#`**: The column number.\n- **Column**: The name of the column.\n- **Non-Null Count**: The number of non-null (non-missing) entries in each column.\n- **Dtype**: The data type of each column.\n- **dtypes: int64(4), object(1)**: Summarizes the data types in the DataFrame; in this example, there are four columns of integers (`int64`) and one column of objects (usually strings).\n- **memory usage: 390.8 KB**: The total memory usage of the DataFrame.\n\n### Usage\n\nUsing `train.info()` is very helpful for:\n\n- Getting a quick overview of the dataset.\n- Checking for missing values.\n- Understanding the data types of each column, which is crucial for data preprocessing and analysis.\n- Estimating the memory usage to understand the dataset's size in memory.","metadata":{}},{"cell_type":"code","source":"# Displaying a concise summary of the DataFrame 'train'\ntrain.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:38.796349Z","iopub.execute_input":"2024-06-07T16:54:38.796737Z","iopub.status.idle":"2024-06-07T16:54:38.827184Z","shell.execute_reply.started":"2024-06-07T16:54:38.796706Z","shell.execute_reply":"2024-06-07T16:54:38.826038Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Detailed Explanation:\n\n1. **Figure Setup**:\n   ```python\n   plt.figure(figsize=(20, 10))\n   sns.set(style=\"whitegrid\")\n   ```\n   - `plt.figure(figsize=(20, 10))`: Creates a new figure with a specified size of 20 inches by 10 inches.\n   - `sns.set(style=\"whitegrid\")`: Sets the aesthetic style of the plots to have a white background with gridlines.\n\n2. **Data Transformation**:\n   ```python\n   grouped_df = train.melt(var_name='Condition_Level', value_name='Category', id_vars=['study_id'])\n   ```\n   - `train.melt()`: Converts the `train` DataFrame from a wide format to a long format.\n     - `var_name='Condition_Level'`: The name of the new column that will contain the column names from `train`.\n     - `value_name='Category'`: The name of the new column that will contain the values from `train`.\n     - `id_vars=['study_id']`: Specifies that `study_id` should remain as an identifier variable.\n\n3. **Creating New Columns**:\n   ```python\n   grouped_df['Level'] = grouped_df['Condition_Level'].apply(lambda x: '_'.join(x.split('_')[-2:]))\n   grouped_df['Condition'] = grouped_df['Condition_Level'].apply(lambda x: '_'.join(x.split('_')[:-2]))\n   ```\n   - `grouped_df['Level']`: A new column created by splitting the `Condition_Level` string on underscores and joining the last two parts.\n   - `grouped_df['Condition']`: Another new column created by splitting the `Condition_Level` string on underscores and joining all parts except the last two.\n\n4. **Plotting**:\n   ```python\n   sns.countplot(data=grouped_df, x='Level', hue='Category')\n   plt.title('Comparing the frequency of all states for each level')\n   plt.xlabel('Level')\n   plt.ylabel('Count')\n   plt.legend(title='Category')\n   plt.xticks(rotation=45)\n   plt.show()\n   ```\n   - `sns.countplot(data=grouped_df, x='Level', hue='Category')`: Creates a count plot showing the frequency of each `Category` for each `Level`.\n   - `plt.title()`, `plt.xlabel()`, `plt.ylabel()`, `plt.legend()`: Customize the title, axis labels, and legend of the plot.\n   - `plt.xticks(rotation=45)`: Rotates the x-axis labels by 45 degrees for better readability.\n   - `plt.show()`: Displays the plot.\n\nThis code processes the data from the `train` DataFrame, transforming it into a format suitable for visualization, and then creates a count plot to compare the frequency of different categories across different levels.","metadata":{}},{"cell_type":"code","source":"# Set up the figure size for the plot to 20 inches by 10 inches\nplt.figure(figsize=(20, 10))\n\n# Set the style of seaborn plots to 'whitegrid', which adds gridlines and a white background to the plot\nsns.set(style=\"whitegrid\")\n\n# Transform the DataFrame 'train' from wide format to long format.\n# 'var_name' is the name of the new column that holds the former column headers (in this case, 'Condition_Level').\n# 'value_name' is the name of the new column that holds the former values (in this case, 'Category').\n# 'id_vars' specifies which column(s) to keep as identifier variables (in this case, 'study_id').\ngrouped_df = train.melt(var_name='Condition_Level', value_name='Category', id_vars=['study_id'])\n\n# Create a new column 'Level' by splitting the 'Condition_Level' string on underscores and joining the last two parts.\ngrouped_df['Level'] = grouped_df['Condition_Level'].apply(lambda x: '_'.join(x.split('_')[-2:]))\n\n# Create a new column 'Condition' by splitting the 'Condition_Level' string on underscores and joining all parts except the last two.\ngrouped_df['Condition'] = grouped_df['Condition_Level'].apply(lambda x: '_'.join(x.split('_')[:-2]))\n\n# Create a count plot to visualize the frequency of each 'Category' at each 'Level'\nsns.countplot(data=grouped_df, x='Level', hue='Category')\n\n# Set the title of the plot\nplt.title('Comparing the frequency of all states for each level')\n\n# Set the label for the x-axis\nplt.xlabel('Level')\n\n# Set the label for the y-axis\nplt.ylabel('Count')\n\n# Add a legend with the title 'Category'\nplt.legend(title='Category')\n\n# Rotate the x-axis labels by 45 degrees for better readability\nplt.xticks(rotation=45)\n\n# Display the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:38.828567Z","iopub.execute_input":"2024-06-07T16:54:38.828890Z","iopub.status.idle":"2024-06-07T16:54:39.596078Z","shell.execute_reply.started":"2024-06-07T16:54:38.828862Z","shell.execute_reply":"2024-06-07T16:54:39.594945Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Detailed Explanation:\n\n1. **Initialize Variables**:\n    ```python\n    conditions = set()\n    levels = set()\n    result = []\n    ```\n    - Initializes empty sets `conditions` and `levels` to store unique conditions and levels.\n    - Initializes an empty list `result` to store intermediate DataFrames.\n\n2. **Iterate Over Columns**:\n    ```python\n    for column_name in train.columns[1:]:\n        counts = train[column_name].value_counts().reset_index()\n        counts.columns = ['Category', 'Count']\n        condition, level = '_'.join(column_name.split('_')[:-2]), '_'.join(column_name.split('_')[-2:])\n        counts['Condition'] = condition\n        counts['Level'] = level\n        conditions.add(condition)\n        levels.add(level)\n        result.append(counts)\n    ```\n    - Iterates over each column in the `train` DataFrame starting from the second column.\n    - For each column, counts the occurrences of each category.\n    - Extracts the condition and level from the column name by splitting the name on underscores.\n    - Adds the extracted condition and level to the counts DataFrame.\n    - Adds the condition and level to their respective sets.\n    - Appends the counts DataFrame to the result list.\n\n3. **Convert Sets to Sorted Lists**:\n    ```python\n    conditions = sorted(list(conditions))\n    levels = sorted(list(levels))\n    ```\n    - Converts the `conditions` and `levels` sets to sorted lists.\n\n4. **Concatenate DataFrames**:\n    ```python\n    final_result_df = pd.concat(result).reset_index(drop=True)\n    ```\n    - Concatenates all DataFrames in the result list into a single DataFrame `final_result_df`.\n\n5. **Set Up Figure for Plots**:\n    ```python\n    plt.figure(figsize=(20, 15))\n    sns.set(style=\"whitegrid\")\n    fig, axes = plt.subplots(3, 2, figsize=(20, 20), constrained_layout=True)\n    axes = axes.flatten()\n    ```\n    - Sets the figure size for the plots.\n    - Sets the seaborn style to 'whitegrid'.\n    - Creates a 3x2 grid of subplots with constrained layout and flattens the array of axes.\n\n6. **Plot Each Condition**:\n    ```python\n    for i, condition in enumerate(conditions):\n        condition_df = final_result_df[final_result_df['Condition'] == condition]\n        sns.barplot(x='Level', y='Count', hue='Category', data=condition_df, ax=axes[i])\n        axes[i].set_title(f'Count categories {condition.replace(\"_\", \" \").title()}')\n        axes[i].set_xlabel('Levels')\n        axes[i].set_ylabel('Count')\n        axes[i].legend(title='Category')\n        axes[i].tick_params(axis='x', rotation=45)\n    ```\n    - Iterates over the sorted conditions and plots each one in a separate subplot.\n    - Filters the `final_result_df` for the current condition.\n    - Creates a bar plot for the current condition.\n    - Sets the title, x-label, y-label, and legend for each subplot.\n    - Rotates the x-axis labels for better readability.\n\n7. **Remove Unused Subplots**:\n    ```python\n    for j in range(len(conditions), len(axes)):\n        fig.delaxes(axes[j])\n    ```\n    - Removes any unused subplots if there are more subplots than conditions.\n\nThis code effectively processes and visualizes the frequency of different categories across various levels for each condition in the `train` DataFrame. The resulting plots provide a comprehensive overview of the data distribution for each condition and level.","metadata":{}},{"cell_type":"code","source":"# Initialize empty sets for conditions and levels, and an empty list to store results\nconditions = set()\nlevels = set()\nresult = []\n\n# Iterate over each column name in the 'train' DataFrame starting from the second column\nfor column_name in train.columns[1:]:\n    # Count the occurrences of each category in the column\n    counts = train[column_name].value_counts().reset_index()\n    counts.columns = ['Category', 'Count']\n    \n    # Extract the condition and level from the column name\n    condition, level = '_'.join(column_name.split('_')[:-2]), '_'.join(column_name.split('_')[-2:])\n    \n    # Add the extracted condition and level to the DataFrame\n    counts['Condition'] = condition\n    counts['Level'] = level\n    \n    # Add the condition and level to their respective sets\n    conditions.add(condition)\n    levels.add(level)\n    \n    # Append the counts DataFrame to the result list\n    result.append(counts)\n\n# Convert the sets to sorted lists\nconditions = sorted(list(conditions))\nlevels = sorted(list(levels))\n\n# Concatenate all DataFrames in the result list into a single DataFrame\nfinal_result_df = pd.concat(result).reset_index(drop=True)\n\n# Set the figure size for the plots\nplt.figure(figsize=(20, 15))\n\n# Set the style of seaborn plots to 'whitegrid'\nsns.set(style=\"whitegrid\")\n\n# Create a 3x2 grid of subplots with constrained layout\nfig, axes = plt.subplots(3, 2, figsize=(20, 20), constrained_layout=True)\naxes = axes.flatten()  # Flatten the array of axes for easy iteration\n\n# Iterate over the sorted conditions and plot each one in a separate subplot\nfor i, condition in enumerate(conditions):\n    # Filter the final result DataFrame for the current condition\n    condition_df = final_result_df[final_result_df['Condition'] == condition]\n    \n    # Create a bar plot for the current condition\n    sns.barplot(x='Level', y='Count', hue='Category', data=condition_df, ax=axes[i])\n    \n    # Set the title and labels for the subplot\n    axes[i].set_title(f'Count categories {condition.replace(\"_\", \" \").title()}')\n    axes[i].set_xlabel('Levels')\n    axes[i].set_ylabel('Count')\n    \n    # Set the legend title\n    axes[i].legend(title='Category')\n    \n    # Rotate the x-axis labels for better readability\n    axes[i].tick_params(axis='x', rotation=45)\n\n# Remove any unused subplots if there are more subplots than conditions\nfor j in range(len(conditions), len(axes)):\n    fig.delaxes(axes[j])","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:39.598866Z","iopub.execute_input":"2024-06-07T16:54:39.599243Z","iopub.status.idle":"2024-06-07T16:54:43.107489Z","shell.execute_reply.started":"2024-06-07T16:54:39.599199Z","shell.execute_reply":"2024-06-07T16:54:43.106332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Detailed Explanation:\n\n1. **Figure Setup**:\n    ```python\n    plt.figure(figsize=(15, 10))\n    sns.set(style=\"whitegrid\")\n    ```\n    - `plt.figure(figsize=(15, 10))`: Sets the figure size to 15 inches by 10 inches for the initial plot.\n    - `sns.set(style=\"whitegrid\")`: Sets the aesthetic style of the plots to have a white background with gridlines.\n\n2. **Creating a Pivot Table**:\n    ```python\n    pivot_table = final_result_df.pivot_table(values='Count', index='Condition', columns=['Level', 'Category'], aggfunc='sum', fill_value=0)\n    ```\n    - `final_result_df.pivot_table()`: Creates a pivot table from the `final_result_df` DataFrame.\n        - `values='Count'`: Specifies that the values to aggregate are in the 'Count' column.\n        - `index='Condition'`: Sets the rows of the pivot table to be the unique values in the 'Condition' column.\n        - `columns=['Level', 'Category']`: Sets the columns of the pivot table to be a combination of 'Level' and 'Category'.\n        - `aggfunc='sum'`: Aggregates the data by summing the 'Count' values.\n        - `fill_value=0`: Fills missing values with 0.\n\n3. **Heatmap Setup**:\n    ```python\n    plt.figure(figsize=(20, 10))\n    sns.heatmap(pivot_table, annot=True, fmt='d', cmap='Blues')\n    ```\n    - `plt.figure(figsize=(20, 10))`: Sets the figure size for the heatmap to 20 inches by 10 inches.\n    - `sns.heatmap(pivot_table, annot=True, fmt='d', cmap='Blues')`: Creates a heatmap from the pivot table.\n        - `pivot_table`: Uses the pivot table as the data source.\n        - `annot=True`: Enables annotation of each cell with its value.\n        - `fmt='d'`: Specifies the format for annotations as integers.\n        - `cmap='Blues'`: Uses the 'Blues' color map for the heatmap.\n\n4. **Customizing the Heatmap**:\n    ```python\n    plt.title('Heatmap for comparing categories by levels and states')\n    plt.xlabel('Levels and Categories')\n    plt.ylabel('Conditions')\n    plt.xticks(rotation=90)\n    plt.show()\n    ```\n    - `plt.title()`: Sets the title of the heatmap.\n    - `plt.xlabel()`: Sets the label for the x-axis.\n    - `plt.ylabel()`: Sets the label for the y-axis.\n    - `plt.xticks(rotation=90)`: Rotates the x-axis labels by 90 degrees for better readability.\n    - `plt.show()`: Displays the heatmap.\n\nThis code effectively transforms the data into a pivot table and visualizes it as a heatmap. The heatmap provides a clear comparison of the frequency counts of different categories across various levels and conditions, with color intensity representing the count values. The annotations make it easy to see the exact counts in each cell.","metadata":{}},{"cell_type":"code","source":"# Set the figure size for the plot to 15 inches by 10 inches\nplt.figure(figsize=(15, 10))\n\n# Set the style of seaborn plots to 'whitegrid'\nsns.set(style=\"whitegrid\")\n\n# Create a pivot table from the final result DataFrame\n# 'values' specifies the column to aggregate ('Count')\n# 'index' specifies the rows of the pivot table ('Condition')\n# 'columns' specifies the columns of the pivot table (a combination of 'Level' and 'Category')\n# 'aggfunc' specifies the aggregation function to apply (sum)\n# 'fill_value' specifies the value to use for missing values (0)\npivot_table = final_result_df.pivot_table(values='Count', index='Condition', columns=['Level', 'Category'], aggfunc='sum', fill_value=0)\n\n# Set the figure size for the heatmap to 20 inches by 10 inches\nplt.figure(figsize=(20, 10))\n\n# Create a heatmap from the pivot table\n# 'annot' enables annotation of each cell with its value\n# 'fmt' specifies the format for annotations ('d' for integers)\n# 'cmap' specifies the color map to use ('Blues')\nsns.heatmap(pivot_table, annot=True, fmt='d', cmap='Blues')\n\n# Set the title of the heatmap\nplt.title('Heatmap for comparing categories by levels and states')\n\n# Set the label for the x-axis\nplt.xlabel('Levels and Categories')\n\n# Set the label for the y-axis\nplt.ylabel('Conditions')\n\n# Rotate the x-axis labels by 90 degrees for better readability\nplt.xticks(rotation=90)\n\n# Display the heatmap\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:43.108779Z","iopub.execute_input":"2024-06-07T16:54:43.109127Z","iopub.status.idle":"2024-06-07T16:54:44.046586Z","shell.execute_reply.started":"2024-06-07T16:54:43.109094Z","shell.execute_reply":"2024-06-07T16:54:44.045424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Detailed Explanation:\n\n1. **Figure Setup**:\n    ```python\n    plt.figure(figsize=(10, 6))\n    ```\n    - `plt.figure(figsize=(10, 6))`: Sets the figure size to 10 inches by 6 inches.\n\n2. **Creating a Scatter Plot**:\n    ```python\n    sns.scatterplot(x='x', y='y', hue='condition', data=labels, palette='viridis')\n    ```\n    - `sns.scatterplot()`: Creates a scatter plot using seaborn.\n        - `x='x'`: Sets the x-axis variable to the 'x' column in the `labels` DataFrame.\n        - `y='y'`: Sets the y-axis variable to the 'y' column in the `labels` DataFrame.\n        - `hue='condition'`: Colors the points based on the 'condition' column in the `labels` DataFrame.\n        - `data=labels`: Specifies the `labels` DataFrame as the data source.\n        - `palette='viridis'`: Uses the 'viridis' color palette for the points.\n\n3. **Customizing the Plot**:\n    ```python\n    plt.title('Scatter Plot of Points Colored by Condition')\n    plt.xlabel('X Coordinate')\n    plt.ylabel('Y Coordinate')\n    ```\n    - `plt.title()`: Sets the title of the plot.\n    - `plt.xlabel()`: Sets the label for the x-axis.\n    - `plt.ylabel()`: Sets the label for the y-axis.\n\n4. **Adding a Legend**:\n    ```python\n    plt.legend(title='Condition', bbox_to_anchor=(1.05, 1), loc='upper left')\n    ```\n    - `plt.legend()`: Adds a legend to the plot.\n        - `title='Condition'`: Sets the title of the legend to 'Condition'.\n        - `bbox_to_anchor=(1.05, 1)`: Positions the legend outside the plot to the right.\n        - `loc='upper left'`: Anchors the legend to the upper left corner of the bounding box.\n\n5. **Enabling Grid Lines**:\n    ```python\n    plt.grid(True)\n    ```\n    - `plt.grid(True)`: Enables grid lines on the plot for better readability.\n\n6. **Displaying the Plot**:\n    ```python\n    plt.show()\n    ```\n    - `plt.show()`: Displays the plot.\n\nThis code creates a scatter plot where the points are colored based on their condition, providing a clear visual representation of how different conditions are distributed in the x-y coordinate space. The legend positioned outside the plot ensures that it does not obscure any data points. The grid lines improve the readability of the plot, making it easier to interpret.","metadata":{}},{"cell_type":"code","source":"# Set the figure size for the plot to 10 inches by 6 inches\nplt.figure(figsize=(10, 6))\n\n# Create a scatter plot using seaborn\n# 'x' specifies the x-axis variable\n# 'y' specifies the y-axis variable\n# 'hue' specifies the variable that will determine the color of the points\n# 'data' specifies the DataFrame containing the data to plot\n# 'palette' specifies the color palette to use for the points\nsns.scatterplot(x='x', y='y', hue='condition', data=labels, palette='viridis')\n\n# Set the title of the plot\nplt.title('Scatter Plot of Points Colored by Condition')\n\n# Set the label for the x-axis\nplt.xlabel('X Coordinate')\n\n# Set the label for the y-axis\nplt.ylabel('Y Coordinate')\n\n# Add a legend with the title 'Condition'\n# 'bbox_to_anchor' specifies the position of the legend outside the plot\n# 'loc' specifies the location of the legend relative to the bounding box\nplt.legend(title='Condition', bbox_to_anchor=(1.05, 1), loc='upper left')\n\n# Enable grid lines on the plot\nplt.grid(True)\n\n# Display the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:44.047999Z","iopub.execute_input":"2024-06-07T16:54:44.048434Z","iopub.status.idle":"2024-06-07T16:54:46.097914Z","shell.execute_reply.started":"2024-06-07T16:54:44.048395Z","shell.execute_reply":"2024-06-07T16:54:46.096970Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Detailed Explanation:\n\n1. **Figure Setup**:\n    ```python\n    plt.figure(figsize=(10, 6))\n    ```\n    - `plt.figure(figsize=(10, 6))`: Sets the figure size to 10 inches by 6 inches.\n\n2. **Creating a Scatter Plot**:\n    ```python\n    sns.scatterplot(x='x', y='y', hue='level', data=labels, palette='tab10')\n    ```\n    - `sns.scatterplot()`: Creates a scatter plot using seaborn.\n        - `x='x'`: Sets the x-axis variable to the 'x' column in the `labels` DataFrame.\n        - `y='y'`: Sets the y-axis variable to the 'y' column in the `labels` DataFrame.\n        - `hue='level'`: Colors the points based on the 'level' column in the `labels` DataFrame.\n        - `data=labels`: Specifies the `labels` DataFrame as the data source.\n        - `palette='tab10'`: Uses the 'tab10' color palette for the points.\n\n3. **Customizing the Plot**:\n    ```python\n    plt.title('Scatter Plot of Points Colored by Level')\n    plt.xlabel('X Coordinate')\n    plt.ylabel('Y Coordinate')\n    ```\n    - `plt.title()`: Sets the title of the plot.\n    - `plt.xlabel()`: Sets the label for the x-axis.\n    - `plt.ylabel()`: Sets the label for the y-axis.\n\n4. **Adding a Legend**:\n    ```python\n    plt.legend(title='Level', bbox_to_anchor=(1.05, 1), loc='upper left')\n    ```\n    - `plt.legend()`: Adds a legend to the plot.\n        - `title='Level'`: Sets the title of the legend to 'Level'.\n        - `bbox_to_anchor=(1.05, 1)`: Positions the legend outside the plot to the right.\n        - `loc='upper left'`: Anchors the legend to the upper left corner of the bounding box.\n\n5. **Enabling Grid Lines**:\n    ```python\n    plt.grid(True)\n    ```\n    - `plt.grid(True)`: Enables grid lines on the plot for better readability.\n\n6. **Displaying the Plot**:\n    ```python\n    plt.show()\n    ```\n    - `plt.show()`: Displays the plot.\n\nThis code creates a scatter plot where the points are colored based on their level, providing a clear visual representation of how different levels are distributed in the x-y coordinate space. The legend positioned outside the plot ensures that it does not obscure any data points. The grid lines improve the readability of the plot, making it easier to interpret.","metadata":{}},{"cell_type":"code","source":"# Set the figure size for the plot to 10 inches by 6 inches\nplt.figure(figsize=(10, 6))\n\n# Create a scatter plot using seaborn\n# 'x' specifies the x-axis variable\n# 'y' specifies the y-axis variable\n# 'hue' specifies the variable that will determine the color of the points\n# 'data' specifies the DataFrame containing the data to plot\n# 'palette' specifies the color palette to use for the points\nsns.scatterplot(x='x', y='y', hue='level', data=labels, palette='tab10')\n\n# Set the title of the plot\nplt.title('Scatter Plot of Points Colored by Level')\n\n# Set the label for the x-axis\nplt.xlabel('X Coordinate')\n\n# Set the label for the y-axis\nplt.ylabel('Y Coordinate')\n\n# Add a legend with the title 'Level'\n# 'bbox_to_anchor' specifies the position of the legend outside the plot\n# 'loc' specifies the location of the legend relative to the bounding box\nplt.legend(title='Level', bbox_to_anchor=(1.05, 1), loc='upper left')\n\n# Enable grid lines on the plot\nplt.grid(True)\n\n# Display the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:46.099133Z","iopub.execute_input":"2024-06-07T16:54:46.099451Z","iopub.status.idle":"2024-06-07T16:54:47.936029Z","shell.execute_reply.started":"2024-06-07T16:54:46.099424Z","shell.execute_reply":"2024-06-07T16:54:47.934947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Detailed Explanation:\n\n1. **Merging with `labels` DataFrame**:\n    ```python\n    merged_data = pd.merge(labels, train, on='study_id', how='left')\n    ```\n    - This line merges the `labels` DataFrame with the `train` DataFrame.\n    - `pd.merge()` is used to merge two DataFrames based on a common column.\n    - `on='study_id'` specifies that the merge should be performed based on the 'study_id' column.\n    - `how='left'` specifies a left join, meaning that all rows from the `labels` DataFrame will be kept, and matching rows from the `train` DataFrame will be added where available. Non-matching rows will have NaN values in the columns from the `train` DataFrame.\n\n2. **Merging with `series` DataFrame**:\n    ```python\n    final_data = pd.merge(merged_data, series[['series_id', 'series_description']], on='series_id', how='left')\n    ```\n    - This line merges the previously merged `merged_data` DataFrame with the `series` DataFrame.\n    - `pd.merge()` is used again to merge two DataFrames based on a common column.\n    - `on='series_id'` specifies that the merge should be performed based on the 'series_id' column.\n    - `how='left'` specifies a left join, meaning that all rows from the `merged_data` DataFrame will be kept, and matching rows from the `series` DataFrame will be added where available. Non-matching rows will have NaN values in the columns from the `series` DataFrame.\n\n3. **Result**:\n    - The resulting DataFrame `final_data` contains the merged information from the `labels`, `train`, and `series` DataFrames, where information from the `train` and `series` DataFrames is added to the `labels` DataFrame based on the common columns 'study_id' and 'series_id', respectively.\n    - Each row in `final_data` corresponds to a combination of a label, training data, and series description, providing a comprehensive dataset for further analysis.","metadata":{}},{"cell_type":"code","source":"# Merge the 'labels' DataFrame with the 'train' DataFrame on the 'study_id' column\n# 'how='left'' specifies to perform a left join, keeping all rows from the 'labels' DataFrame\n# 'on='study_id'' specifies the column to join on\nmerged_data = pd.merge(labels, train, on='study_id', how='left')\n\n# Merge the 'merged_data' DataFrame with the 'series' DataFrame on the 'series_id' column\n# 'how='left'' specifies to perform a left join, keeping all rows from the 'merged_data' DataFrame\n# 'on='series_id'' specifies the column to join on\nfinal_data = pd.merge(merged_data, series[['series_id', 'series_description']], on='series_id', how='left')","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:47.937310Z","iopub.execute_input":"2024-06-07T16:54:47.937633Z","iopub.status.idle":"2024-06-07T16:54:48.058129Z","shell.execute_reply.started":"2024-06-07T16:54:47.937603Z","shell.execute_reply":"2024-06-07T16:54:48.056995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Explanation:\n\n1. **Figure Setup**:\n   - `plt.figure(figsize=(15, 10))`: Sets up the figure for the plot with a size of 15 inches by 10 inches.\n\n2. **Iterating Over Unique Series Descriptions**:\n   - `for i, series in enumerate(final_data['series_description'].unique(), 1):`\n     - Iterates over each unique series description in the 'series_description' column of the `final_data` DataFrame.\n     - `enumerate()` is used to get both the index (`i`) and the value (`series`) from the unique series descriptions. The index starts from 1.\n\n3. **Creating Subplots**:\n   - `plt.subplot(2, 2, i)`: Creates subplots in a 2x2 grid. The current subplot index is determined by `i` in the loop.\n\n4. **Subsetting Data**:\n   - `subset = final_data[final_data['series_description'] == series]`: Subsets the data for the current series description.\n\n5. **Creating Scatter Plots**:\n   - `sns.scatterplot(x='x', y='y', hue='level', data=subset, palette='tab10', s=10)`: Creates a scatter plot for the current series description. Points are colored by spine level.\n     - `x='x', y='y'`: Specifies the x and y coordinates.\n     - `hue='level'`: Colors the points based on the spine level.\n     - `data=subset`: Uses the subset of data for the current series description.\n     - `palette='tab10'`: Sets the color palette to 'tab10'.\n     - `s=10`: Sets the size of the points to 10.\n\n6. **Customizing Plot Titles and Labels**:\n   - `plt.title(f'Scatter Plot for {series} Colored by Spine Level')`: Sets the title of the subplot.\n   - `plt.xlabel('X Coordinate')`: Sets the label for the x-axis.\n   - `plt.ylabel('Y Coordinate')`: Sets the label for the y-axis.\n\n7. **Adding Legend and Grid Lines**:\n   - `plt.legend(title='Spine Level', bbox_to_anchor=(1.05, 1), loc='upper left')`: Adds a legend to the subplot.\n     - `bbox_to_anchor=(1.05, 1)`: Positions the legend outside the plot to the right.\n   - `plt.grid(True)`: Enables grid lines on the subplot.\n\n8. **Adjusting Layout and Displaying Plot**:\n   - `plt.tight_layout()`: Adjusts the layout of the subplots to prevent overlap.\n   - `plt.show()`: Displays the plot.","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(15, 10))\n\n# Iterate over unique series descriptions in the final_data DataFrame\nfor i, series in enumerate(final_data['series_description'].unique(), 1):\n    plt.subplot(2, 2, i)  # Create a subplot with 2 rows and 2 columns\n    subset = final_data[final_data['series_description'] == series]  # Subset data for the current series\n    sns.scatterplot(x='x', y='y', hue='level', data=subset, palette='tab10', s=10)  # Create scatter plot\n    plt.title(f'Scatter Plot for {series} Colored by Spine Level')  # Set title for the subplot\n    plt.xlabel('X Coordinate')  # Set label for x-axis\n    plt.ylabel('Y Coordinate')  # Set label for y-axis\n    plt.legend(title='Spine Level', bbox_to_anchor=(1.05, 1), loc='upper left')  # Add legend\n    plt.grid(True)  # Enable grid lines\n\nplt.tight_layout()  # Adjust subplot layout to prevent overlap\nplt.show()  # Show the plot","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:48.059393Z","iopub.execute_input":"2024-06-07T16:54:48.059710Z","iopub.status.idle":"2024-06-07T16:54:50.758811Z","shell.execute_reply.started":"2024-06-07T16:54:48.059682Z","shell.execute_reply":"2024-06-07T16:54:50.757628Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Explanation:\n\n1. **Function Definition**:\n   - `visualize_series_images(base_path, study_id, series_id, title, num_cols=5)`: Defines a function to visualize DICOM images from a specified series. It takes parameters such as the base path, study ID, series ID, title, and the number of columns for image display.\n\n2. **Constructing Series Path**:\n   - `series_path = os.path.join(base_path, str(study_id), str(series_id))`: Constructs the path to the series directory using the base path, study ID, and series ID.\n\n3. **Listing DICOM Files**:\n   - `image_files = [os.path.join(series_path, f) for f in os.listdir(series_path) if f.endswith('.dcm')][:10]`: Lists all DICOM files in the series directory, limiting to the first 10 files.\n\n4. **Calculating Rows and Columns**:\n   - `num_images = len(image_files)`: Calculates the total number of images in the series.\n   - `num_rows = ceil(num_images / num_cols)`: Calculates the number of rows required to display the images based on the specified number of columns.\n\n5. **Creating Subplots**:\n   - `fig, axs = plt.subplots(num_rows, num_cols, figsize=(num_cols*3, num_rows*3))`: Creates a figure and a set of subplots based on the calculated number of rows and columns.\n\n6. **Flattening Axes**:\n   - `axs = axs.flatten() if num_images > 1 else [axs]`: Flattens the subplots into a 1D array if there is more than one image, otherwise keeps it as a 2D array.\n\n7. **Displaying Images**:\n   - Iterates through each image file and corresponding axis, reads the DICOM file using `pydicom`, displays the pixel array of the DICOM image on the axis, and turns off the axis for better visualization.\n\n8. **Adjusting Layout and Spacing**:\n   - `plt.tight_layout()`: Adjusts the layout of the subplots to prevent overlap.\n   - `plt.subplots_adjust(top=0.95)`: Adjusts the spacing to accommodate the title.\n\n9. **Displaying the Plot**:\n   - `plt.show()`: Displays the plot containing the visualized DICOM images.\n\n10. **Iterating Through Series**:\n    - Iterates through each series and calls the `visualize_series_images` function to visualize its images.","metadata":{}},{"cell_type":"code","source":"def visualize_series_images(base_path, study_id, series_id, title, num_cols=5):\n    # Construct the path to the series directory using base path, study ID, and series ID\n    series_path = os.path.join(base_path, str(study_id), str(series_id))\n    \n    # List all DICOM files in the series directory, limiting to the first 10 files\n    image_files = [os.path.join(series_path, f) for f in os.listdir(series_path) if f.endswith('.dcm')][:10]\n    \n    # Get the total number of images in the series\n    num_images = len(image_files)\n    \n    # Calculate the number of rows required to display the images\n    num_rows = ceil(num_images / num_cols)\n    \n    # Create a figure and a set of subplots\n    fig, axs = plt.subplots(num_rows, num_cols, figsize=(num_cols*3, num_rows*3))\n    \n    # If there is only one image, axs should be a 1D array instead of a 2D array\n    axs = axs.flatten() if num_images > 1 else [axs]\n    \n    # Iterate through each image file and corresponding axis\n    for ax, img_path in zip(axs, image_files):\n        # Read the DICOM file using pydicom\n        dicom_content = pydicom.dcmread(img_path)\n        \n        # Display the pixel array of the DICOM image on the axis\n        ax.imshow(dicom_content.pixel_array, cmap='gray')\n        \n        # Turn off axis for better visualization\n        ax.axis('off')\n    \n    # If there are fewer images than the total number of subplots, turn off the remaining axes\n    for ax in axs[num_images:]:\n        ax.axis('off')\n    \n    # Set a common title for all subplots\n    plt.suptitle(title)\n    \n    # Adjust layout and spacing of subplots\n    plt.tight_layout()\n    plt.subplots_adjust(top=0.95)\n    \n    # Display the plot\n    plt.show()\n\n# Iterate through each series and visualize its images\nfor series in series_ids:\n    visualize_series_images(base_path, study_id, series['series_id'], series['description'])","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:50.760660Z","iopub.execute_input":"2024-06-07T16:54:50.761371Z","iopub.status.idle":"2024-06-07T16:54:56.819924Z","shell.execute_reply.started":"2024-06-07T16:54:50.761330Z","shell.execute_reply":"2024-06-07T16:54:56.818658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Explanation:\n\n1. **Function Definition**:\n   - `visualize_multiple_dicoms_with_colormaps(dicom_file_paths)`: Defines a function to visualize DICOM images with different colormaps. It takes a list of DICOM file paths as input.\n\n2. **Defining Colormaps**:\n   - `cmaps = [...]`: Defines a list of colormaps to use for visualizing the DICOM images.\n\n3. **Defining Rows and Columns**:\n   - `n_rows = 3`, `n_cols = 5`: Specifies the number of rows and columns for the subplots grid.\n\n4. **Iterating Through DICOM File Paths**:\n   - `for dicom_file_path in dicom_file_paths:`: Iterates through each DICOM file path provided in the input list.\n\n5. **Reading DICOM Data**:\n   - `dicom_data = pydicom.dcmread(dicom_file_path)`: Reads the DICOM data from the file path using `pydicom`.\n\n6. **Extracting Pixel Array**:\n   - `pixel_array = dicom_data.pixel_array`: Extracts the pixel array from the DICOM data.\n\n7. **Creating Subplots**:\n   - `fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, 12))`: Creates a figure and a set of subplots with the specified number of rows and columns.\n\n8. **Iterating Through Subplots and Colormaps**:\n   - `for ax, cmap in zip(axes.flatten(), cmaps):`: Iterates through each subplot and corresponding colormap.\n     - `ax.imshow(pixel_array, cmap=cmap)`: Displays the DICOM image with the current colormap on the subplot.\n     - `ax.set_title(cmap)`: Sets the title of the subplot to the name of the colormap.\n     - `ax.axis('off')`: Turns off the axis for better visualization.\n\n9. **Removing Excess Subplots**:\n   - `for ax in axes.flatten()[len(cmaps):]:`: Removes excess subplots if there are more colormaps than subplots in the grid.\n\n10. **Setting Title and Displaying Plot**:\n    - `plt.suptitle(f'DICOM Image with Different Colormaps\\n{dicom_file_path}')`: Sets a common title for all subplots, indicating the DICOM file path.\n    - `plt.show()`: Displays the plot containing the visualized DICOM images with different colormaps.\n\nThis function provides a convenient way to visualize DICOM images using various colormaps, allowing for better interpretation and analysis of medical imaging data.","metadata":{}},{"cell_type":"code","source":"def visualize_multiple_dicoms_with_colormaps(dicom_file_paths):\n    # Define a list of colormaps to use\n    cmaps = ['gray', 'bone', 'viridis', 'plasma', 'inferno', 'magma', 'cividis', 'hot', 'cool', 'twilight', 'twilight_shifted', 'jet']\n    \n    # Define the number of rows and columns for subplots\n    n_rows = 3\n    n_cols = 5\n\n    # Iterate through each DICOM file path provided\n    for dicom_file_path in dicom_file_paths:\n        # Read the DICOM data\n        dicom_data = pydicom.dcmread(dicom_file_path)\n        \n        # Get the pixel array from the DICOM data\n        pixel_array = dicom_data.pixel_array\n\n        # Create a figure and a set of subplots\n        fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, 12))\n\n        # Iterate through each subplot and corresponding colormap\n        for ax, cmap in zip(axes.flatten(), cmaps):\n            # Display the DICOM image with the current colormap\n            ax.imshow(pixel_array, cmap=cmap)\n            \n            # Set the title of the subplot to the colormap name\n            ax.set_title(cmap)\n            \n            # Turn off axis for better visualization\n            ax.axis('off')\n\n        # Remove excess subplots if there are more colormaps than subplots\n        for ax in axes.flatten()[len(cmaps):]:\n            fig.delaxes(ax)\n\n        # Set a common title for all subplots\n        plt.suptitle(f'DICOM Image with Different Colormaps\\n{dicom_file_path}')\n        \n        # Display the plot\n        plt.show()\n\n# Call the function with the list of DICOM file paths\nvisualize_multiple_dicoms_with_colormaps(dicom_file_paths)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:54:56.821785Z","iopub.execute_input":"2024-06-07T16:54:56.822185Z","iopub.status.idle":"2024-06-07T16:55:05.182796Z","shell.execute_reply.started":"2024-06-07T16:54:56.822148Z","shell.execute_reply":"2024-06-07T16:55:05.181664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Explanation:\n\n1. **Function Definition**:\n   - `display_image_with_annotations(base_path, data)`: Defines a function to display DICOM images with annotations. It takes the base path to the DICOM images and the data containing annotations as input.\n\n2. **Iterating Through Data**:\n   - `for index, row in data[:10].iterrows():`: Iterates through the first 10 rows of the provided data.\n\n3. **Extracting Information**:\n   - Extracts relevant information such as study ID, series ID, instance number, condition, level, coordinates (x, y), and series description from each row of the data.\n\n4. **Constructing Image Path**:\n   - Constructs the path to the DICOM image file using the base path, study ID, series ID, and instance number.\n\n5. **Checking Image Existence**:\n   - Checks if the DICOM image file exists at the specified path.\n\n6. **Reading DICOM Data and Creating Plot**:\n   - Reads the DICOM data using `pydicom.dcmread()` and creates a figure and axis for the plot.\n\n7. **Displaying DICOM Image**:\n   - Displays the DICOM image on the axis using `ax.imshow()` with a grayscale colormap.\n\n8. **Adding Annotations**:\n   - Adds a circle annotation at the specified coordinates (x, y) using `ax.add_patch()`.\n\n9. **Setting Title and Turning Off Axis**:\n   - Sets a title for the plot indicating the series description, condition, and level. Turns off the axis for better visualization using `plt.title()` and `plt.axis('off')`.\n\n10. **Displaying Plot**:\n    - Displays the plot using `plt.show()`.\n\n11. **Handling Missing Files**:\n    - Prints a message if the DICOM image file is not found at the specified path.\n\nThis function provides a visual representation of DICOM images with annotations, making it easier to interpret and analyze medical imaging data.","metadata":{}},{"cell_type":"code","source":"from matplotlib.patches import Circle\n\ndef display_image_with_annotations(base_path, data):\n    # Iterate through the first 10 rows of the provided data\n    for index, row in data[:10].iterrows():\n        # Extract relevant information from the row\n        study_id = row['study_id']\n        series_id = row['series_id']\n        instance_number = row['instance_number'] \n        condition = row['condition']\n        level = row['level']\n        x, y = row['x'], row['y']\n        description = row['series_description']\n        \n        # Construct the path to the DICOM image\n        image_path = os.path.join(base_path, str(study_id), str(series_id), f\"{instance_number}.dcm\")\n        \n        # Check if the DICOM image file exists\n        if os.path.exists(image_path):\n            # Read the DICOM data\n            dicom_content = pydicom.dcmread(image_path)\n            \n            # Create a figure and axis for the plot\n            fig, ax = plt.subplots(1, figsize=(7, 10))\n            \n            # Display the DICOM image on the axis\n            ax.imshow(dicom_content.pixel_array, cmap='gray')\n            \n            # Add a circle annotation at the specified coordinates\n            ax.add_patch(Circle((x, y), radius=10, color='red', fill=False))\n            \n            # Set title and turn off axis for better visualization\n            plt.title(f\"{description}\\n{condition} at {level}\")\n            plt.axis('off')\n            \n            # Display the plot\n            plt.show()\n        else:\n            # Print a message if the DICOM image file is not found\n            print(f\"File not found: {image_path}\")\n\n# Call the function with the base path and final data\ndisplay_image_with_annotations(base_path, final_data)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:55:05.184139Z","iopub.execute_input":"2024-06-07T16:55:05.184479Z","iopub.status.idle":"2024-06-07T16:55:09.175501Z","shell.execute_reply.started":"2024-06-07T16:55:05.184451Z","shell.execute_reply":"2024-06-07T16:55:09.174467Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Explanation:\n\n1. **Function Definition**:\n   - `create_gif_from_series(study_id, series_id, description)`: Defines a function to create a GIF animation from a series of DICOM images. It takes the study ID, series ID, and description as input.\n\n2. **Image Processing**:\n   - The function iterates through each file in the series directory and checks if it's a DICOM image file (`if file_name.endswith('.dcm')`).\n   - It reads the DICOM file using `pydicom.dcmread()` and extracts the pixel array from the DICOM data (`img_array = ds.pixel_array`).\n\n3. **Normalization**:\n   - Pixel values are normalized to the range [0, 255] using `(img_array - img_array.min()) / (img_array.max() - img_array.min()) * 255`.\n   - Pixel values are then converted to unsigned integers (`img_array.astype('uint8')`) for compatibility with imageio.\n\n4. **GIF Creation**:\n   - The normalized image arrays are appended to the `images` list.\n   - The function constructs the path to save the GIF file (`gif_path`) using the description provided.\n   - The `imageio.mimsave()` function is used to save the list of images as a GIF file with a frame duration of 0.1 seconds.\n\n5. **Iteration Through Series**:\n   - The function is called within a loop that iterates through each series in the `series_ids` list.\n   - For each series, the function is called with the study ID, series ID, and description to create a GIF animation.","metadata":{}},{"cell_type":"code","source":"def create_gif_from_series(study_id, series_id, description):\n    images = []  # Initialize an empty list to store image arrays\n    series_path = os.path.join(base_path, str(study_id), str(series_id))  # Construct the path to the series directory\n    \n    # Iterate through each file in the series directory\n    for file_name in sorted(os.listdir(series_path)):\n        # Check if the file is a DICOM image\n        if file_name.endswith('.dcm'):\n            file_path = os.path.join(series_path, file_name)  # Construct the full path to the DICOM image file\n            ds = pydicom.dcmread(file_path)  # Read the DICOM file\n            img_array = ds.pixel_array  # Extract the pixel array from the DICOM data\n            \n            # Normalize pixel values to the range [0, 255] and convert to unsigned integer\n            img_array = (img_array - img_array.min()) / (img_array.max() - img_array.min()) * 255\n            img_array = img_array.astype('uint8')\n            \n            images.append(img_array)  # Append the normalized image array to the list of images\n    \n    gif_path = os.path.join(f\"{description}.gif\")  # Construct the path to save the GIF file\n    imageio.mimsave(gif_path, images, duration=0.1)  # Save the list of images as a GIF with a frame duration of 0.1 seconds\n\n\n# Iterate through each series and create a GIF animation\nfor series in series_ids:\n    create_gif_from_series(study_id, series['series_id'], series['description'])\n","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:55:09.179798Z","iopub.execute_input":"2024-06-07T16:55:09.180182Z","iopub.status.idle":"2024-06-07T16:55:11.069660Z","shell.execute_reply.started":"2024-06-07T16:55:09.180146Z","shell.execute_reply":"2024-06-07T16:55:11.068661Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"1. **`extract_relevant_dicom_metadata` Function**:\n   - This function takes a DICOM file path as input.\n   - It reads the DICOM file using `pydicom.dcmread()` to obtain the DICOM data.\n   - Relevant metadata attributes are extracted from the DICOM data, such as Series Description, Slice Thickness, Instance Number, etc.\n   - The extracted metadata is stored in a dictionary.\n   - If a particular attribute is not present in the DICOM data, it is assigned a value of 'N/A'.\n   - Finally, the metadata dictionary is returned.\n\n2. **`display_relevant_dicom_metadata` Function**:\n   - This function takes a list of DICOM file paths as input.\n   - It iterates through each DICOM file path in the list.\n   - For each DICOM file path, it calls `extract_relevant_dicom_metadata` to extract metadata.\n   - The extracted metadata for each DICOM file is appended to a list.\n   - After processing all DICOM files, the list of metadata dictionaries is converted into a pandas DataFrame.\n   - The DataFrame is displayed using `display.display(df)`.\n\nThese functions allow you to easily extract and display relevant metadata from multiple DICOM files. The metadata can be useful for analyzing and understanding the contents of DICOM images, aiding in medical image interpretation and analysis.","metadata":{}},{"cell_type":"code","source":"from matplotlib.patches import Circle\n\ndef display_image_with_annotations(base_path, data):\n    \"\"\"\n    Display DICOM images with annotations.\n    \n    Args:\n    - base_path (str): Base directory path containing DICOM images.\n    - data (DataFrame): DataFrame containing image annotations.\n    \n    Returns:\n    - None\n    \"\"\"\n    for index, row in data[:10].iterrows():  # Display annotations for the first 10 images\n        study_id = row['study_id']\n        series_id = row['series_id']\n        instance_number = row['instance_number'] \n        condition = row['condition']\n        level = row['level']\n        x, y = row['x'], row['y']\n        description = row['series_description']\n        \n        # Construct the path to the DICOM image\n        image_path = os.path.join(base_path, str(study_id), str(series_id), f\"{instance_number}.dcm\")\n        \n        if os.path.exists(image_path):  # Check if the DICOM image file exists\n            dicom_content = pydicom.dcmread(image_path)  # Read the DICOM data\n            fig, ax = plt.subplots(1, figsize=(7, 10))\n            ax.imshow(dicom_content.pixel_array, cmap='gray')  # Display the DICOM image\n            ax.add_patch(Circle((x, y), radius=10, color='red', fill=False))  # Add a circle annotation\n            plt.title(f\"{description}\\n{condition} at {level}\")  # Set title\n            plt.axis('off')  # Turn off axis\n            plt.show()  # Display the plot\n        else:\n            print(f\"File not found: {image_path}\")  # Print message if the DICOM image file is not found\n\n# Call the function to display DICOM images with annotations\ndisplay_image_with_annotations(base_path, final_data)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:55:11.071085Z","iopub.execute_input":"2024-06-07T16:55:11.071425Z","iopub.status.idle":"2024-06-07T16:55:15.195399Z","shell.execute_reply.started":"2024-06-07T16:55:11.071397Z","shell.execute_reply":"2024-06-07T16:55:15.194290Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"These imports cover a range of functionalities:\n\n- **NumPy (`np`)**: A library for numerical computing, providing support for arrays, matrices, and mathematical functions.\n- **Pandas (`pd`)**: A library for data manipulation and analysis, providing data structures and functions to work with structured data.\n- **OS**: A module for interacting with the operating system, used here for file management.\n- **Time**: A module for working with time-related functions, though it's not used explicitly in this code.\n- **Matplotlib.pyplot (`plt`)**: A library for creating static, interactive, and animated visualizations in Python.\n- **Plotly Express (`px`)**: A high-level interface for creating interactive plots with Plotly, which allows for interactive exploration of data.\n- **Seaborn (`sns`)**: A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive and informative statistical graphics.\n- **PyDicom (`dicom`)**: A library for working with DICOM (Digital Imaging and Communications in Medicine) files, commonly used in medical imaging.\n- **Sklearn (`sklearn`)**: A machine learning library that provides simple and efficient tools for data mining and data analysis, including data splitting functions like `train_test_split()`.\n\nOverall, these libraries cover a wide range of functionalities commonly used in data analysis, visualization, and machine learning tasks.","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport time\n\n# For creating static plots\nimport matplotlib.pyplot as plt\n\n# For creating interactive plots\nimport plotly.express as px\n\n# For creating statistical visualizations\nimport seaborn as sns\n\n# For working with DICOM files\nimport pydicom as dicom\n\n# For splitting data into train and test sets\nfrom sklearn.model_selection import train_test_split","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:57:57.214412Z","iopub.execute_input":"2024-06-07T16:57:57.215677Z","iopub.status.idle":"2024-06-07T16:57:58.250985Z","shell.execute_reply.started":"2024-06-07T16:57:57.215632Z","shell.execute_reply":"2024-06-07T16:57:58.249855Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here's what each line does:\n\n- **df_train_main**: Reads the training dataset containing main information such as study ID, series ID, instance number, and classification labels.\n- **df_train_label**: Reads the training dataset containing label coordinates, which likely specify the coordinates of regions of interest in the images.\n- **df_train_desc**: Reads the training dataset containing series descriptions, which provide additional information about the imaging series.\n- **df_test_desc**: Reads the test dataset containing series descriptions, similar to the training dataset but for test data.\n- **df_sub**: Reads the sample submission file, which provides the format for submitting predictions for the competition.","metadata":{}},{"cell_type":"code","source":"# Read the training dataset containing main information\ndf_train_main = pd.read_csv('../input/rsna-2024-lumbar-spine-degenerative-classification/train.csv')\n\n# Read the training dataset containing label coordinates\ndf_train_label = pd.read_csv('../input/rsna-2024-lumbar-spine-degenerative-classification/train_label_coordinates.csv')\n\n# Read the training dataset containing series descriptions\ndf_train_desc = pd.read_csv('../input/rsna-2024-lumbar-spine-degenerative-classification/train_series_descriptions.csv')\n\n# Read the test dataset containing series descriptions\ndf_test_desc = pd.read_csv('../input/rsna-2024-lumbar-spine-degenerative-classification/test_series_descriptions.csv')\n\n# Read the sample submission file\ndf_sub = pd.read_csv('../input/rsna-2024-lumbar-spine-degenerative-classification/sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:58:50.518350Z","iopub.execute_input":"2024-06-07T16:58:50.519267Z","iopub.status.idle":"2024-06-07T16:58:50.630100Z","shell.execute_reply.started":"2024-06-07T16:58:50.519208Z","shell.execute_reply":"2024-06-07T16:58:50.629042Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Without seeing the actual data, I can't provide a detailed breakdown of the contents. However, I can infer that `df_train_main` likely contains information about the training dataset, including various features related to lumbar spine degenerative classification. Let's assume it includes columns like study ID, series ID, instance number, and classification labels. Calling `.head(2)` on the DataFrame displays the first two rows, giving a glimpse of the dataset's structure. If you have any specific questions or need further analysis, feel free to ask!","metadata":{}},{"cell_type":"code","source":"df_train_main.head(2)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T16:59:19.868429Z","iopub.execute_input":"2024-06-07T16:59:19.868822Z","iopub.status.idle":"2024-06-07T16:59:19.890314Z","shell.execute_reply.started":"2024-06-07T16:59:19.868791Z","shell.execute_reply":"2024-06-07T16:59:19.889288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- **Study ID**: This column likely identifies a unique study or patient visit. Each row with the same study ID may correspond to different images or scans taken during the same study.\n  \n- **Series ID**: This column likely identifies a unique series within a study. A series typically represents a set of images or scans acquired using the same imaging parameters or technique. For example, different series may correspond to different imaging modalities (e.g., MRI, CT) or different anatomical views.\n\n- **Instance Number**: This column might indicate the position or order of an image within a series. Each row with the same series ID may have a different instance number, indicating a different image within the same series.\n\n- **Classification Labels**: This column likely contains labels or annotations related to lumbar spine degenerative classification. Each row may represent a specific image or scan, and the classification labels provide information about the presence or severity of degenerative conditions in the lumbar spine.\n\nBy examining the first two rows of the DataFrame, we can get an initial understanding of the dataset's structure and the type of information it contains. This helps in further data exploration, analysis, and modeling tasks. If you need more detailed information about specific columns or further analysis, feel free to ask!","metadata":{}},{"cell_type":"code","source":"df_train_main.head(2)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T17:00:08.993752Z","iopub.execute_input":"2024-06-07T17:00:08.994164Z","iopub.status.idle":"2024-06-07T17:00:09.017010Z","shell.execute_reply.started":"2024-06-07T17:00:08.994131Z","shell.execute_reply":"2024-06-07T17:00:09.015867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"1. **Using Melt to Transform Columns into Rows**:\n   - The DataFrame `df_train_main` is being unpivoted using the `melt()` function, where the 'study_id' column is kept as an identifier variable (`id_vars='study_id'`), and all other columns are melted into two new columns: 'condition' and 'status'.\n   - This transformation effectively transforms multiple columns into rows, making the data more tidy and suitable for analysis.\n\n2. **Calculating Frequency Table**:\n   - The resulting DataFrame (`df_unpivoted`) is then grouped by the 'condition' column, and the frequency of each 'status' value is calculated using `value_counts(normalize=True)`.\n   - The `unstack()` function is applied to reshape the data, making 'status' values become columns, and 'condition' values become rows.\n   - The `fill_value=0` parameter fills missing values with 0.\n\n3. **Resetting Index**:\n   - The index of the resulting DataFrame (`frequency_table`) is reset so that 'condition' becomes a column again.\n\n4. **Renaming Columns**:\n   - Column names are renamed to match the expected format or convention. For example, 'Moderate' might be renamed to 'moderate'.\n\n5. **Processing Sample Submission Data**:\n   - The 'condition' values for the sample submission DataFrame (`df_sub`) are extracted from the 'row_id' column using a regular expression (`str.extract(r'_(.*)')`). This likely extracts the condition labels from row IDs in the submission file.\n   - The resulting 'condition' values are merged with the frequency table DataFrame (`frequency_table`) based on the 'condition' column.\n   - Finally, the merged DataFrame is subset to include only relevant columns ('row_id', 'normal_mild', 'moderate', 'severe'), possibly for submission format requirements.\n\nOverall, this code segment performs data transformation, aggregation, and merging operations to prepare the data for further analysis or submission in a specific format.","metadata":{}},{"cell_type":"code","source":"# Using melt to transform columns into rows\ndf_unpivoted = df_train_main.melt(id_vars='study_id', var_name='condition', value_name='status')\n\n# Calculating frequency table\nfrequency_table = df_unpivoted.groupby('condition')['status'].value_counts(normalize=True).unstack(fill_value=0)\n\n# Resetting the index to make 'condition' a column again\nfrequency_table = frequency_table.reset_index()\n\n# Renaming columns for consistency\nfrequency_table.rename(columns={'Moderate': 'moderate', 'Normal/Mild': 'normal_mild', 'Severe': 'severe'}, inplace=True)\n\n# Extracting condition values from row IDs in the sample submission data\ndf_sub['condition'] = df_sub['row_id'].str.extract(r'_(.*)')\n\n# Merging condition values with the frequency table\ndf_sub = pd.merge(df_sub[['row_id', 'condition']], frequency_table, on='condition', how='inner')[['row_id', 'normal_mild', 'moderate', 'severe']]","metadata":{"execution":{"iopub.status.busy":"2024-06-07T17:01:16.834470Z","iopub.execute_input":"2024-06-07T17:01:16.834871Z","iopub.status.idle":"2024-06-07T17:01:16.878557Z","shell.execute_reply.started":"2024-06-07T17:01:16.834842Z","shell.execute_reply":"2024-06-07T17:01:16.877688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This code will output the column labels of the DataFrame df_train_main. It provides a quick way to see the names of all the columns in the DataFrame. If you need further assistance or analysis of these columns, feel free to ask!","metadata":{}},{"cell_type":"code","source":"df_train_main.columns","metadata":{"execution":{"iopub.status.busy":"2024-06-07T17:01:49.536800Z","iopub.execute_input":"2024-06-07T17:01:49.537193Z","iopub.status.idle":"2024-06-07T17:01:49.544426Z","shell.execute_reply.started":"2024-06-07T17:01:49.537163Z","shell.execute_reply":"2024-06-07T17:01:49.543282Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"1. **Loading Necessary Libraries**:\n   - The code starts by importing the `pandas` library for data manipulation and the `os` module for interacting with the operating system.\n\n2. **Defining Test Images Directory**:\n   - The variable `test_images_dir` is defined as the path to the directory containing the test images.\n\n3. **Extracting Unique IDs**:\n   - The code extracts unique IDs from the filenames of the test images directory. It assumes that filenames have a common format where the ID is the part before the period (e.g., 'ID123.jpg' -> 'ID123').\n   - These unique IDs are stored in the list `unique_ids`.\n\n4. **Generating Row IDs for Submission**:\n   - The code defines a list of conditions based on the expected conditions from the training dataset.\n   - It then generates row IDs for submission by combining each unique ID with each condition. This creates a list of row IDs that cover all combinations of unique IDs and conditions.\n\n5. **Creating DataFrame for Submission**:\n   - A DataFrame named `df_submission` is created using the generated row IDs.\n   - Three columns ('normal_mild', 'moderate', 'severe') are added to represent predictions for each condition. For demonstration purposes, equal probabilities (0.333333) are assigned to each condition for all row IDs.\n\n6. **Displaying the DataFrame**:\n   - Finally, the DataFrame `df_submission` is displayed, showing the structure and content of the sample submission.\n\nThis code serves as a template for generating a sample submission DataFrame, which can be further customized with actual predictions for each condition based on model outputs.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport os\n\n# Load the sample submission or create an empty DataFrame if you have the structure\n# Assuming df_submission is your DataFrame name with 'row_id' and prediction columns\n\n# Define the path to your test images directory\ntest_images_dir = \"/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/test_images\"\n\n# Get all unique IDs from the filenames in the test images directory\ntest_ids = [filename.split('.')[0] for filename in os.listdir(test_images_dir)]\nunique_ids = list(set(test_ids))\n\n# Generate the row_ids needed for submission by repeating each unique_id for each condition from df_train_main\nconditions = ['left_neural_foraminal_narrowing_l1_l2',\n              'left_neural_foraminal_narrowing_l2_l3',\n              'left_neural_foraminal_narrowing_l3_l4',\n              'left_neural_foraminal_narrowing_l4_l5',\n              'left_neural_foraminal_narrowing_l5_s1',\n              'left_subarticular_stenosis_l1_l2',\n              'left_subarticular_stenosis_l2_l3',\n              'left_subarticular_stenosis_l3_l4',\n              'left_subarticular_stenosis_l4_l5',\n              'left_subarticular_stenosis_l5_s1',\n              'right_neural_foraminal_narrowing_l1_l2',\n              'right_neural_foraminal_narrowing_l2_l3',\n              'right_neural_foraminal_narrowing_l3_l4',\n              'right_neural_foraminal_narrowing_l4_l5',\n              'right_neural_foraminal_narrowing_l5_s1',\n              'right_subarticular_stenosis_l1_l2',\n              'right_subarticular_stenosis_l2_l3',\n              'right_subarticular_stenosis_l3_l4',\n              'right_subarticular_stenosis_l4_l5',\n              'right_subarticular_stenosis_l5_s1',\n              'spinal_canal_stenosis_l1_l2',\n              'spinal_canal_stenosis_l2_l3',\n              'spinal_canal_stenosis_l3_l4',\n              'spinal_canal_stenosis_l4_l5',\n              'spinal_canal_stenosis_l5_s1']\n\nrow_ids = [f\"{id}_{condition}\" for id in unique_ids for condition in conditions]\n\n# Create DataFrame\ndf_submission = pd.DataFrame(row_ids, columns=['row_id'])\ndf_submission['normal_mild'] = 0.333333\ndf_submission['moderate'] = 0.333333\ndf_submission['severe'] = 0.333333\n\ndf_submission","metadata":{"execution":{"iopub.status.busy":"2024-06-07T17:03:01.211690Z","iopub.execute_input":"2024-06-07T17:03:01.212769Z","iopub.status.idle":"2024-06-07T17:03:01.242720Z","shell.execute_reply.started":"2024-06-07T17:03:01.212722Z","shell.execute_reply":"2024-06-07T17:03:01.241371Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n\nHere's a breakdown of the code:\n\n```python\n# Save the DataFrame to a CSV file for submission\ndf_submission.to_csv('submission.csv', index=False)\n```\n\nExplanation:\n\n- `df_submission.to_csv()`: This method is used to save the DataFrame `df_submission` to a CSV file.\n  \n- `'submission.csv'`: This specifies the filename for the CSV file. In this case, it's named 'submission.csv'.\n\n- `index=False`: This parameter specifies whether to include the DataFrame's index as a column in the CSV file. By setting it to `False`, we're indicating that we don't want to include the index. In many cases, the index doesn't carry meaningful information and is omitted from the CSV file to keep it clean.\n\nOnce executed, this line of code will create a CSV file named 'submission.csv' containing the data from the DataFrame `df_submission`, ready for submission in the competition or task.","metadata":{}},{"cell_type":"code","source":"# Save the DataFrame to a CSV file for submission\ndf_submission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-06-07T17:03:32.135724Z","iopub.execute_input":"2024-06-07T17:03:32.136792Z","iopub.status.idle":"2024-06-07T17:03:32.145015Z","shell.execute_reply.started":"2024-06-07T17:03:32.136757Z","shell.execute_reply":"2024-06-07T17:03:32.143968Z"},"trusted":true},"execution_count":null,"outputs":[]}]}