Table of Contents\n\n- [1. Introduction](#1)\n- [2. Imports](#2)\n- [3. EDA](#3)\n - [3.1 Train Data](#3.1)\n - [3.1.1 Train Data Distribution ](#3.1.1)\n - [3.2 Code Cell Analysis](#3.2)\n - [3.2.1 Code Cells Length Distribution ](#3.2.1)\n - [3.2.2 Code Cells WordCloud ](#3.2.2)\n - [3.3 Markdown Cell Analysis](#3.2)\n - [3.3.1 Markdown Cells Length Distribution ](#3.3.1)\n - [3.3.2 Markdown Cells WordCloud ](#3.3.2)\n - [3.4 Notebooks Analysis](#3.4)\n - [3.4.1 Code Cell Count Analysis ](#3.4.1)\n - [3.4.2 Markdown Cell Count Analysis ](#3.4.2)\n - [3.4.3 Minimum Cell Count Analysis ](#3.4.3)\n ","metadata":{}},{"cell_type":"markdown","source":"\n# **Introduction **","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"The goal of this competition is to understand the relationship between code and comments in Python notebooks. You are challenged to reconstruct the order of markdown cells in a given notebook based on the order of the code cells, demonstrating comprehension of which natural language references which code.
\n\nPredictions are evaluated by the Kendall tau correlation between predicted cell orders and ground truth cell orders accumulated across the entire collection of test set notebooks.
\n\nCheck more about Kendall tau correlation - https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient","metadata":{}},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"\n# **IMPORTS **","metadata":{}},{"cell_type":"code","source":"import os\nimport json\nimport wordcloud\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport matplotlib.pyplot as plt\nimport plotly.graph_objects as go\nfrom tqdm.notebook import tqdm, trange","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-05-13T08:15:40.681244Z","iopub.execute_input":"2022-05-13T08:15:40.681633Z","iopub.status.idle":"2022-05-13T08:15:42.369167Z","shell.execute_reply.started":"2022-05-13T08:15:40.681542Z","shell.execute_reply":"2022-05-13T08:15:42.368147Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Loading Data : \n","metadata":{}},{"cell_type":"markdown","source":"Pandas Dataframe used is created by [Darien Schettler](https://www.kaggle.com/dschettler8845). Link to dataset - https://www.kaggle.com/datasets/dschettler8845/ai4code-train-dataframe","metadata":{}},{"cell_type":"code","source":"## train dataframes\ndf = pd.read_csv(\"../input/ai4code-train-dataframe/train.csv\", index_col= [0,1])\ndf.dropna(inplace = True)\n\ndf_ancestors = pd.read_csv('../input/AI4Code/train_ancestors.csv', index_col='id')\ndf_orders = pd.read_csv(\"../input/AI4Code/train_orders.csv\",index_col='id',squeeze=True,).str.split()","metadata":{"execution":{"iopub.status.busy":"2022-05-13T08:15:42.370911Z","iopub.execute_input":"2022-05-13T08:15:42.371153Z","iopub.status.idle":"2022-05-13T08:17:21.784666Z","shell.execute_reply.started":"2022-05-13T08:15:42.371123Z","shell.execute_reply":"2022-05-13T08:17:21.783751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"\n# **EDA**","metadata":{}},{"cell_type":"markdown","source":"\n 📌 Observations :
\n\n* A total of 139256 notebooks are provided train set.
\n* A total of 4 notebooks are provided test set. This will be replaced with a hidden test set for scoring
\n* There are total of 146300 cells in train the train dataframe constructed which include two types of cell_type.
\n* Two types of cell_type - code and markdown.\n* Almost 2/3rd of the training data consist of Code Cells and remaining 1/3rd consist of Markdown Cells.\n
","metadata":{}},{"cell_type":"code","source":"print(f\"\\033[94mNumber of notebooks present in train set = \",len(os.listdir(\"../input/AI4Code/train\")))\nprint(f\"\\033[94mNumber of notebooks present in test set = \",len(os.listdir(\"../input/AI4Code/test\")))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:17:21.786043Z","iopub.execute_input":"2022-05-13T08:17:21.786305Z","iopub.status.idle":"2022-05-13T08:17:23.973114Z","shell.execute_reply.started":"2022-05-13T08:17:21.786275Z","shell.execute_reply":"2022-05-13T08:17:23.972432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Quick View of train data : ","metadata":{}},{"cell_type":"markdown","source":"\n 📌 `df` is a Multi-level indexing Padnas Dataframe with 2 index - id and cell_id.
\nLearn more about Multi-level indexing Padnas Dataframe - https://pandas.pydata.org/docs/user_guide/advanced.html\n
","metadata":{}},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-13T08:17:23.975043Z","iopub.execute_input":"2022-05-13T08:17:23.975597Z","iopub.status.idle":"2022-05-13T08:17:24.001099Z","shell.execute_reply.started":"2022-05-13T08:17:23.975559Z","shell.execute_reply":"2022-05-13T08:17:24.000274Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Train data distribution : ","metadata":{}},{"cell_type":"code","source":"code_df = df[df[\"cell_type\"] == \"code\"]\nmkd_df = df[df[\"cell_type\"] == \"markdown\"]\n\n\nprint(f'\\033[94mNumber of Code Cells: {len(code_df)}')\nprint(f'\\033[94mNumber of Markdown Cells: {len(mkd_df)}')\n\nlabels=['Code Cells', 'Markdown Cells']\nvalues= [len(code_df), len(mkd_df)]\ncolors = ['#DE3163', '#58D68D']\n\nfig = go.Figure(data=[go.Pie(\n labels=labels, \n values=values, \n pull=[0.1, 0 ],\n marker=dict(colors=colors, \n line=dict(color='#000000', \n width=2))\n)])\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:17:24.002502Z","iopub.execute_input":"2022-05-13T08:17:24.003098Z","iopub.status.idle":"2022-05-13T08:17:26.420096Z","shell.execute_reply.started":"2022-05-13T08:17:24.003048Z","shell.execute_reply":"2022-05-13T08:17:26.419283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"\n## Code cells analysis : ","metadata":{}},{"cell_type":"markdown","source":"\n 📌 Observations:
\n\n* Mean Length for Code Cells is 25 words
\n* Max words in a Code Cell is 74589 words
\n* There are many outliers in Code Cells
\n
","metadata":{}},{"cell_type":"markdown","source":"### Sample Code Cell: ","metadata":{}},{"cell_type":"code","source":"print(f'\\033[94m')\nprint(code_df.iloc[0][\"source\"])","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:17:26.421285Z","iopub.execute_input":"2022-05-13T08:17:26.421545Z","iopub.status.idle":"2022-05-13T08:17:26.427815Z","shell.execute_reply.started":"2022-05-13T08:17:26.421513Z","shell.execute_reply":"2022-05-13T08:17:26.427092Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Code cells Length Distribution : ","metadata":{}},{"cell_type":"code","source":"code_lengths = np.array([len(code_df[\"source\"][i].split()) for i in range(len(code_df))])\nprint(f'\\033[94m Min Code Cells Length = ', min(code_lengths))\nprint(f'\\033[94m Mean Code cells Length = ', round(np.mean(code_lengths),2))\nprint(f'\\033[94m Max Code Cells Length = ', max(code_lengths))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:17:26.429286Z","iopub.execute_input":"2022-05-13T08:17:26.429614Z","iopub.status.idle":"2022-05-13T08:18:08.917541Z","shell.execute_reply.started":"2022-05-13T08:17:26.429580Z","shell.execute_reply":"2022-05-13T08:18:08.916740Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig,ax= plt.subplots(figsize= (18,6))\nplt.boxplot(code_lengths, vert = False)\nplt.xlabel(\"Lenght of Code Cells\");","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:18:08.918828Z","iopub.execute_input":"2022-05-13T08:18:08.919043Z","iopub.status.idle":"2022-05-13T08:18:09.951152Z","shell.execute_reply.started":"2022-05-13T08:18:08.919016Z","shell.execute_reply":"2022-05-13T08:18:09.950566Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Code cells WordCloud : ","metadata":{}},{"cell_type":"code","source":"wordcloud_notes = wordcloud.WordCloud(stopwords=wordcloud.STOPWORDS, max_font_size=120, max_words=5000,\n width = 600, height = 400,\n background_color='white').generate(\"\".join(code_df[\"source\"][:1000]))\nfig, ax = plt.subplots(figsize=(14,10))\nax.imshow(wordcloud_notes, interpolation='bilinear')\nax.set_axis_off()\nplt.imshow(wordcloud_notes);","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:18:09.952230Z","iopub.execute_input":"2022-05-13T08:18:09.952563Z","iopub.status.idle":"2022-05-13T08:18:11.844326Z","shell.execute_reply.started":"2022-05-13T08:18:09.952534Z","shell.execute_reply":"2022-05-13T08:18:11.843456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"## Markdown cells analysis : ","metadata":{}},{"cell_type":"markdown","source":"\n 📌 Observations:
\n\n* Mean Length for Markdown Cells is 29 words
\n* Max words in a Markdown Cell is 38939 words
\n* There are many outliers in Markdown Cells as well.
\n
","metadata":{}},{"cell_type":"markdown","source":"### Sample Markdown Cell: ","metadata":{}},{"cell_type":"code","source":"print(f'\\033[94m')\nprint(mkd_df.iloc[59][\"source\"])","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:18:11.846648Z","iopub.execute_input":"2022-05-13T08:18:11.846910Z","iopub.status.idle":"2022-05-13T08:18:11.852968Z","shell.execute_reply.started":"2022-05-13T08:18:11.846879Z","shell.execute_reply":"2022-05-13T08:18:11.852199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Markdown Cells Length Distribution : ","metadata":{"execution":{"iopub.status.busy":"2022-05-11T23:30:22.719828Z","iopub.execute_input":"2022-05-11T23:30:22.720332Z","iopub.status.idle":"2022-05-11T23:30:22.764389Z","shell.execute_reply.started":"2022-05-11T23:30:22.720276Z","shell.execute_reply":"2022-05-11T23:30:22.763352Z"}}},{"cell_type":"code","source":"mkd_lengths = np.array([len(mkd_df [\"source\"][i].split()) for i in range(len(mkd_df))])\nprint(f'\\033[94m Min Markdown Cells Length = ', min(mkd_lengths))\nprint(f'\\033[94m Mean Markdown cells Length = ', round(np.mean(mkd_lengths),2))\nprint(f'\\033[94m Max Markdown Cells Length = ', max(mkd_lengths))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:18:11.854156Z","iopub.execute_input":"2022-05-13T08:18:11.854742Z","iopub.status.idle":"2022-05-13T08:18:34.306932Z","shell.execute_reply.started":"2022-05-13T08:18:11.854695Z","shell.execute_reply":"2022-05-13T08:18:34.306099Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig,ax= plt.subplots(figsize= (18,6))\nplt.boxplot(mkd_lengths, vert = False)\nplt.xlabel(\"Lenght of Markdown Cells\");","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:18:34.308346Z","iopub.execute_input":"2022-05-13T08:18:34.308669Z","iopub.status.idle":"2022-05-13T08:18:34.938936Z","shell.execute_reply.started":"2022-05-13T08:18:34.308628Z","shell.execute_reply":"2022-05-13T08:18:34.938407Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Markdown Cells WordCloud : ","metadata":{}},{"cell_type":"code","source":"wordcloud_notes = wordcloud.WordCloud(stopwords=wordcloud.STOPWORDS, max_font_size=120, max_words=5000,\n width = 600, height = 400,\n background_color='white').generate(\"\".join(mkd_df[\"source\"][:1000]))\nfig, ax = plt.subplots(figsize=(14,10))\nax.imshow(wordcloud_notes, interpolation='bilinear')\nax.set_axis_off()\nplt.imshow(wordcloud_notes);","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:18:34.939796Z","iopub.execute_input":"2022-05-13T08:18:34.940105Z","iopub.status.idle":"2022-05-13T08:18:37.047184Z","shell.execute_reply.started":"2022-05-13T08:18:34.940078Z","shell.execute_reply":"2022-05-13T08:18:37.046591Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"\n## Notebooks Analysis: ","metadata":{}},{"cell_type":"markdown","source":"\n 📌 Observations:
\n\n* Minimum count for both Code Cells and Markdown cells across all notebooks is 1.
\n* Mean Code cells count across all notebooks is 30 cells
\n* Mean Markdown cells count across all notebooks is 15 cells
\n* Max count of Code cells and Markdown cells across all notebooks is 809 cells and 537 cells respectively.
\n \n
","metadata":{}},{"cell_type":"code","source":"## loading code_cell counts from notebooks \nnotebook_ids = [notebook[:-5] for notebook in os.listdir(\"../input/AI4Code/train\")]\ncode_counts= []\nmarkdown_counts= [] \nfor i in trange(len(notebook_ids)):\n temp_df = df.loc[(notebook_ids[i])]\n code_counts.append((temp_df[\"cell_type\"] == \"code\").sum())\n markdown_counts.append((temp_df[\"cell_type\"] == \"markdown\").sum())\n\ncounts_df = pd.DataFrame(data = np.array([notebook_ids, code_counts, markdown_counts, ]).T, columns = [\"notebook_id\", \"code_count\", \"markdown_count\"])\ncounts_df[\"markdown_count\"] = counts_df[\"markdown_count\"].astype(str).astype(int)\ncounts_df[\"code_count\"] = counts_df[\"code_count\"].astype(str).astype(int)\ncounts_df[\"total_count\"] = counts_df[\"code_count\"] + counts_df[\"markdown_count\"]\nprint(f'\\033[94m Minimum Cell count in any notebook', counts_df[\"total_count\"].min())\nprint(f'\\033[94m Maximum Cell count in any notebook', counts_df[\"total_count\"].max())\nprint(f'\\033[94m Mean of Cell counts across all notebooks', round(counts_df[\"total_count\"].mean(), 2 ))\ncounts_df.head()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:18:37.048112Z","iopub.execute_input":"2022-05-13T08:18:37.048437Z","iopub.status.idle":"2022-05-13T08:39:57.807464Z","shell.execute_reply.started":"2022-05-13T08:18:37.048410Z","shell.execute_reply":"2022-05-13T08:39:57.806425Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Outlier Notebooks Analysis: ","metadata":{}},{"cell_type":"code","source":"k = 100\ntop_k = counts_df.sort_values(by = [\"total_count\"], ascending=False)[:k]\nfig = px.bar(data_frame=top_k, \n x = \"notebook_id\" ,\n y = [ \"code_count\", \"markdown_count\"], \n color_discrete_sequence=['#DE3163', '#58D68D']\n \n )\nfig.update_layout(\n title={\n 'text': \"Cell Type Count analysis for top 100 cell count notebooks(OUTLIERS)\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'},\n xaxis_title=\"Notebook ID\",\n yaxis_title=\"Count\",\n template=\"plotly_white\"\n \n)\nfig.update_traces(marker_line_color='black',\n marker_line_width=0.9,opacity = 0.9)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-05-13T09:06:18.787158Z","iopub.execute_input":"2022-05-13T09:06:18.788143Z","iopub.status.idle":"2022-05-13T09:06:18.931347Z","shell.execute_reply.started":"2022-05-13T09:06:18.788100Z","shell.execute_reply":"2022-05-13T09:06:18.930450Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n### Code Cell Count Analysis: ","metadata":{}},{"cell_type":"code","source":"print(f'\\033[94m Minimum Code Cell count in any notebook', counts_df[\"code_count\"].min())\nprint(f'\\033[94m Maximum Code Cell count in any notebook', counts_df[\"code_count\"].max())\nprint(f'\\033[94m Mean of Code Cell counts across all notebooks', round(counts_df[\"code_count\"].mean(), 2 ))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T08:39:57.809035Z","iopub.execute_input":"2022-05-13T08:39:57.809663Z","iopub.status.idle":"2022-05-13T08:39:57.819614Z","shell.execute_reply.started":"2022-05-13T08:39:57.809623Z","shell.execute_reply":"2022-05-13T08:39:57.818738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Code Cell Count Distribution across all notebooks: ","metadata":{}},{"cell_type":"code","source":"fig = px.histogram(data_frame=counts_df, \n x= \"code_count\",\n color_discrete_sequence=[\"#DE3163\"],\n marginal=\"violin\")\nfig.update_layout(\n title={\n 'text': \"Code Cell Count Distribution\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'},\n xaxis_title=\"Code Cells\",\n yaxis_title=\"Count\",\n showlegend=False,\n template=\"plotly_white\"\n)\nfig.show()\nfig = px.histogram(data_frame=counts_df[counts_df[\"code_count\"]<100], \n x= \"code_count\",\n color_discrete_sequence=[\"#58D68D\"],\n marginal=\"violin\")\nfig.update_layout(\n title={\n 'text': \"Code Cell Count Distribution (COUNTS < 100 )\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'},\n xaxis_title=\"Code Cells\",\n yaxis_title=\"Count\",\n showlegend=False,\n template=\"plotly_white\"\n)\nfig.show()","metadata":{"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"\n### Markdown Cell Count Analysis: ","metadata":{}},{"cell_type":"code","source":"print(f'\\033[94m Minimum Markdown Cell count in any notebook', counts_df[\"markdown_count\"].min())\nprint(f'\\033[94m Maximum Markdown Cell count in any notebook', counts_df[\"markdown_count\"].max())\nprint(f'\\033[94m Mean of Markdown Cell counts across all notebooks', round(counts_df[\"markdown_count\"].mean(), 2 ))","metadata":{"execution":{"iopub.status.busy":"2022-05-12T15:54:02.941955Z","iopub.execute_input":"2022-05-12T15:54:02.942267Z","iopub.status.idle":"2022-05-12T15:54:02.952449Z","shell.execute_reply.started":"2022-05-12T15:54:02.942235Z","shell.execute_reply":"2022-05-12T15:54:02.951422Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Markdown Cell Count Distribution across all notebooks: ","metadata":{}},{"cell_type":"code","source":"fig = px.histogram(data_frame=counts_df, \n x= \"markdown_count\",\n color_discrete_sequence=[\"#DE3163\"],\n marginal=\"violin\")\nfig.update_layout(\n title={\n 'text': \"Markdown Cell Count Distribution\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'},\n xaxis_title=\"Markdown Cells\",\n yaxis_title=\"Count\",\n showlegend=False,\n template=\"plotly_white\"\n)\n\nfig.show()\nfig = px.histogram(data_frame=counts_df[counts_df[\"markdown_count\"]<100], \n x= \"markdown_count\",\n color_discrete_sequence=[\"#58D68D\"],\n marginal=\"violin\")\nfig.update_layout(\n title={\n 'text': \"Markdown Cell Count Distribution (COUNTS < 100 )\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'},\n xaxis_title=\"Markdown Cells\",\n yaxis_title=\"Count\",\n showlegend=False,\n template=\"plotly_white\"\n)\nfig.show()","metadata":{"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"\n## Minimum Cell Count Analysis: ","metadata":{}},{"cell_type":"code","source":"either_one = counts_df[(counts_df[\"markdown_count\"] == 1) | (counts_df[\"code_count\"] == 1 )] \nboth_one = counts_df[(counts_df[\"markdown_count\"] == 1) & (counts_df[\"code_count\"] == 1 )] \ncode_count_one = counts_df[counts_df[\"code_count\"] == 1 ]\nmarkdown_count_one = counts_df[counts_df[\"markdown_count\"] == 1 ]\n\nprint(f\"\\033[94mTotal notebook with either 1 code cell or 1 markdown cell = \", len(either_one))\nprint(f\"\\033[94mTotal notebook with both 1 code cell and 1 markdown cell = \", len(both_one ))\nprint(f\"\\033[94mNotebook counts with only 1 code cell = \" ,len(code_count_one))\nprint(f\"\\033[94mNotebook counts with only 1 markdown cell = \" ,len(markdown_count_one))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-12T16:33:02.142244Z","iopub.execute_input":"2022-05-12T16:33:02.142548Z","iopub.status.idle":"2022-05-12T16:33:02.168784Z","shell.execute_reply.started":"2022-05-12T16:33:02.142518Z","shell.execute_reply":"2022-05-12T16:33:02.16746Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Code cells count vs Markdown cells count: ","metadata":{}},{"cell_type":"code","source":"fig = px.scatter(data_frame=counts_df, \n x = \"code_count\", \n y = \"markdown_count\", \n size = \"code_count\",\n color_discrete_sequence=[\"#DE3163\"])\nfig.add_shape(type='line',\n x0=0,\n y0=0,\n x1=800,\n y1=800,\n line=dict(color='Black'),\n xref='x',\n yref='y',name = \"X=Y line\"\n )\nfig.update_layout(\n title={\n 'text': \"Code Cell Counts vs Markdown Cell Counts\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'},\n xaxis_title=\"Code Cell Counts\",\n yaxis_title=\"Markdown Cell Counts\",\n showlegend=False,\n template=\"plotly_white\"\n)\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-05-13T09:08:46.292780Z","iopub.execute_input":"2022-05-13T09:08:46.293065Z","iopub.status.idle":"2022-05-13T09:08:47.379123Z","shell.execute_reply.started":"2022-05-13T09:08:46.293036Z","shell.execute_reply":"2022-05-13T09:08:47.378162Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⬆️Back to Table of Contents ⬆️","metadata":{}},{"cell_type":"markdown","source":"\n \n \n###
Thank you for reading🙂\n### If you have any feedback or find anything wrong, please let me know!\n","metadata":{}}]}