diff --git a/lesson-2-data-exploration-and-preparation/demo/pandas_profiling/genres_mod.parquet b/lesson-2-data-exploration-and-preparation/demo/ydata_profiling/genres_mod.parquet similarity index 100% rename from lesson-2-data-exploration-and-preparation/demo/pandas_profiling/genres_mod.parquet rename to lesson-2-data-exploration-and-preparation/demo/ydata_profiling/genres_mod.parquet diff --git a/lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling.ipynb b/lesson-2-data-exploration-and-preparation/demo/ydata_profiling/ydata_profiling.ipynb similarity index 94% rename from lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling.ipynb rename to lesson-2-data-exploration-and-preparation/demo/ydata_profiling/ydata_profiling.ipynb index 590ee51c..0f59b3ee 100644 --- a/lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling.ipynb +++ b/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)" ] }, { diff --git a/lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling_solution.ipynb b/lesson-2-data-exploration-and-preparation/demo/ydata_profiling/ydata_profiling_solution.ipynb similarity index 95% rename from lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling_solution.ipynb rename to lesson-2-data-exploration-and-preparation/demo/ydata_profiling/ydata_profiling_solution.ipynb index 0eb8160e..40793d08 100644 --- a/lesson-2-data-exploration-and-preparation/demo/pandas_profiling/pandas_profiling_solution.ipynb +++ b/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)" ] }, { diff --git a/lesson-2-data-exploration-and-preparation/exercises/exercise_4/README.md b/lesson-2-data-exploration-and-preparation/exercises/exercise_4/README.md index 9c483884..e8397cdc 100644 --- a/lesson-2-data-exploration-and-preparation/exercises/exercise_4/README.md +++ b/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. - + \ No newline at end of file diff --git a/lesson-2-data-exploration-and-preparation/exercises/exercise_4/solution/EDA.ipynb b/lesson-2-data-exploration-and-preparation/exercises/exercise_4/solution/EDA.ipynb index 117cb016..e28fe729 100644 --- a/lesson-2-data-exploration-and-preparation/exercises/exercise_4/solution/EDA.ipynb +++ b/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" + ")" ] }, { diff --git a/lesson-2-data-exploration-and-preparation/exercises/exercise_5/README.md b/lesson-2-data-exploration-and-preparation/exercises/exercise_5/README.md index 6e804628..91386423 100644 --- a/lesson-2-data-exploration-and-preparation/exercises/exercise_5/README.md +++ b/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