refactor: update pandas-profiling references to ydata-profiling

lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling.ipynb → lesson-2-data-exploration-and-preparation/demo/ydata_profiling/ydata_profiling.ipynb

@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "78cee852",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas_profiling\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import ydata_profiling"
    ]
   },
   {
@@ -42,12 +42,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "80e4ccd5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "profile = pandas_profiling.ProfileReport(df)"
+    "profile = ydata_profiling.ProfileReport(df)"
    ]
   },
   {

lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling_solution.ipynb → lesson-2-data-exploration-and-preparation/demo/ydata_profiling/ydata_profiling_solution.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "upset-partner",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas_profiling\n",
+    "import pandas as pd\n",
     "import wandb\n",
-    "import pandas as pd"
+    "import ydata_profiling"
    ]
   },
   {
@@ -43,12 +43,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "adaptive-tuition",
    "metadata": {},
    "outputs": [],
    "source": [
-    "profile = pandas_profiling.ProfileReport(df)"
+    "profile = ydata_profiling.ProfileReport(df)"
    ]
   },
   {

lesson-2-data-exploration-and-preparation/exercises/exercise_4/README.md

@@ -31,7 +31,7 @@ wandb artifact put \
 
 3. Create a notebook and call it ``EDA``
 
-4. Within the notebook, import the relevant libraries (seaborn, pandas, wandb, pandas profiling), 
+4. Within the notebook, import the relevant libraries (seaborn, pandas, wandb, ydata-profiling), 
    then create a W&B run.
    NOTE: Remember to add the ``save_source=True`` option to ``wandb.init``
 
@@ -45,9 +45,9 @@ wandb artifact put \
 
 6. Generate a profile and note the warnings:
    ```python
-   from pandas_profiling import ProfileReport
+   from ydata_profiling import ProfileReport
 
-   profile = ProfileReport(df, title="Pandas Profiling Report", explorative=True)
+   profile = ProfileReport(df, title="YData Profiling Report", explorative=True)
    profile.to_widgets()
    ```
 
@@ -74,4 +74,4 @@ wandb artifact put \
 
 8. Go to W&B, navigate to the run you just completed. You will see an option `{}` in the left
    panel. Click on it to see the uploaded Jupyter notebook.
-
+

lesson-2-data-exploration-and-preparation/exercises/exercise_4/solution/EDA.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,12 +11,12 @@
     "import seaborn as sns\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "from pandas_profiling import ProfileReport"
+    "from ydata_profiling import ProfileReport"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -51,8 +51,7 @@
     "run = wandb.init(\n",
     "    project=\"exercise_4\", \n",
     "    save_code=True\n",
-    ")\n",
-    "\n"
+    ")"
    ]
   },
   {

lesson-2-data-exploration-and-preparation/exercises/exercise_5/README.md

@@ -35,8 +35,8 @@ Hints:
    - pip:
        - wandb==0.10.21
    ```
-3. You do NOT need to generate the profiles from pandas-profiling (and you also do not need
-   pandas-profiling as a dependency in ``conda.yml``)
+3. You do NOT need to generate the profiles from ydata-profiling (and you also do not need
+   ydata-profiling as a dependency in ``conda.yml``)
 4. Save the cleaned data in a new artifact on W&B called ``preprocessed_data.csv``
 5. We are going to use the created artifact several times in the following exercises. Verify that
    you have an artifact called ``preprocessed_data.csv`` under the project ``exercise_5``, so the