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‎data-analysis/README.md‎

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11
# Using Python for Data Analysis
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3-
This folder contains completed notebooks and other files used in the Real Python tutorial on [Using Python for Data Analysis](https://realpython.com/python-for-data-analysis/).
3+
This folder contains completed notebooks and other files used in the Real Python tutorial [Python for Data Analysis: A Practical Guide](https://realpython.com/python-for-data-analysis/).
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**The following files are included:**
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‎data-analysis/data_analysis_findings.ipynb‎

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@@ -166,7 +166,8 @@
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"import pandas as pd\n",
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"\n",
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"james_bond_data_html = pd.read_html(\n",
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" \"https://en.wikipedia.org/wiki/List_of_James_Bond_novels_and_short_stories\"\n",
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" \"https://en.wikipedia.org/wiki/List_of_James_Bond_novels_and_short_stories\",\n",
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" storage_options={\"User-Agent\": \"Mozilla/5.0\"},\n",
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")\n",
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"james_bond_tables = james_bond_data_html[1].convert_dtypes()"
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]
@@ -305,7 +306,7 @@
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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
308-
" .replace(\"[$,]\", \"\", regex=True)\n",
309+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" )\n",
@@ -327,17 +328,17 @@
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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
330-
" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" movie_budget=lambda data: (\n",
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" data[\"movie_budget\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" )\n",
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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" movie_budget=lambda data: (\n",
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" data[\"movie_budget\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" film_length=lambda data: (\n",
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" data[\"film_length\"].str.removesuffix(\"mins\").astype(\"Int64\")\n",
385+
" data[\"film_length\"].str.removesuffix(\" mins\").astype(\"Int64\")\n",
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" ),\n",
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" )\n",
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")"
@@ -442,21 +443,21 @@
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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" movie_budget=lambda data: (\n",
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" data[\"movie_budget\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" film_length=lambda data: (\n",
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" data[\"film_length\"].str.removesuffix(\"mins\").astype(\"Int64\")\n",
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" data[\"film_length\"].str.removesuffix(\" mins\").astype(\"Int64\")\n",
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" ),\n",
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" release_date=lambda data: pd.to_datetime(\n",
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" data[\"release_date\"], format=\"%B, %Y\"\n",
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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" movie_budget=lambda data: (\n",
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" data[\"movie_budget\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" * 1000\n",
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" ),\n",
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" film_length=lambda data: (\n",
547-
" data[\"film_length\"].str.removesuffix(\"mins\").astype(\"Int64\")\n",
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" data[\"film_length\"].str.removesuffix(\" mins\").astype(\"Int64\")\n",
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" ),\n",
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" release_date=lambda data: pd.to_datetime(\n",
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" data[\"release_date\"], format=\"%B, %Y\"\n",
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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" movie_budget=lambda data: (\n",
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" data[\"movie_budget\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" * 1000\n",
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" ),\n",
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" film_length=lambda data: (\n",
615-
" data[\"film_length\"].str.removesuffix(\"mins\").astype(\"Int64\")\n",
616+
" data[\"film_length\"].str.removesuffix(\" mins\").astype(\"Int64\")\n",
616617
" ),\n",
617618
" release_date=lambda data: pd.to_datetime(\n",
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" data[\"release_date\"], format=\"%B, %Y\"\n",
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" .assign(\n",
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" income_usa=lambda data: (\n",
664665
" data[\"income_usa\"]\n",
665-
" .replace(\"[$,]\", \"\", regex=True)\n",
666+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
666667
" .astype(\"Float64\")\n",
667668
" ),\n",
668669
" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
670-
" .replace(\"[$,]\", \"\", regex=True)\n",
671+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" movie_budget=lambda data: (\n",
674675
" data[\"movie_budget\"]\n",
675-
" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" * 1000\n",
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" ),\n",
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" film_length=lambda data: (\n",
680-
" data[\"film_length\"].str.removesuffix(\"mins\").astype(\"Int64\")\n",
681+
" data[\"film_length\"].str.removesuffix(\" mins\").astype(\"Int64\")\n",
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" ),\n",
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" release_date=lambda data: pd.to_datetime(\n",
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" data[\"release_date\"], format=\"%B, %Y\"\n",
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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
742+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
746-
" .replace(\"[$,]\", \"\", regex=True)\n",
747+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
748749
" ),\n",
749750
" movie_budget=lambda data: (\n",
750751
" data[\"movie_budget\"]\n",
751-
" .replace(\"[$,]\", \"\", regex=True)\n",
752+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
752753
" .astype(\"Float64\")\n",
753754
" * 1000\n",
754755
" ),\n",
755756
" film_length=lambda data: (\n",
756757
" data[\"film_length\"]\n",
757-
" .str.removesuffix(\"mins\")\n",
758+
" .str.removesuffix(\" mins\")\n",
758759
" .astype(\"Int64\")\n",
759760
" .replace(1200, 120)\n",
760761
" ),\n",
@@ -820,23 +821,23 @@
820821
" .assign(\n",
821822
" income_usa=lambda data: (\n",
822823
" data[\"income_usa\"]\n",
823-
" .replace(\"[$,]\", \"\", regex=True)\n",
824+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
824825
" .astype(\"Float64\")\n",
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" ),\n",
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" income_world=lambda data: (\n",
827828
" data[\"income_world\"]\n",
828-
" .replace(\"[$,]\", \"\", regex=True)\n",
829+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
829830
" .astype(\"Float64\")\n",
830831
" ),\n",
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" movie_budget=lambda data: (\n",
832833
" data[\"movie_budget\"]\n",
833-
" .replace(\"[$,]\", \"\", regex=True)\n",
834+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
834835
" .astype(\"Float64\")\n",
835836
" * 1000\n",
836837
" ),\n",
837838
" film_length=lambda data: (\n",
838839
" data[\"film_length\"]\n",
839-
" .str.removesuffix(\"mins\")\n",
840+
" .str.removesuffix(\" mins\")\n",
840841
" .astype(\"Int64\")\n",
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" .replace(1200, 120)\n",
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" ),\n",
@@ -1059,7 +1060,7 @@
10591060
"name": "python",
10601061
"nbconvert_exporter": "python",
10611062
"pygments_lexer": "ipython3",
1062-
"version": "3.12.0"
1063+
"version": "3.14.5"
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}
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},
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"nbformat": 4,

‎data-analysis/data_analysis_results.ipynb‎

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" .assign(\n",
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" income_usa=lambda data: (\n",
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" data[\"income_usa\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
57+
" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
5858
" .astype(\"Float64\")\n",
5959
" ),\n",
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" income_world=lambda data: (\n",
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" data[\"income_world\"]\n",
62-
" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" ),\n",
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" movie_budget=lambda data: (\n",
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" data[\"movie_budget\"]\n",
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" .replace(\"[$,]\", \"\", regex=True)\n",
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" .replace(r\"[$,\\s]\", \"\", regex=True)\n",
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" .astype(\"Float64\")\n",
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" * 1000\n",
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" ),\n",
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" film_length=lambda data: (\n",
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" data[\"film_length\"]\n",
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" .str.removesuffix(\"mins\")\n",
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" .str.removesuffix(\" mins\")\n",
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" .astype(\"Int64\")\n",
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" ),\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.5"
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}
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},
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"nbformat": 4,

‎data-analysis/james_bond_data_cleansed.csv‎

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Original file line numberDiff line numberDiff line change
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1-
bond_actor,bond_kills,car_manufacturer,film_length,imdb,income_usa,income_world,martinis_consumed,movie_budget,movie_title,release_date,rotten_tomatoes,release_year
2-
Sean Connery,4,Sunbeam,110,7.3,16067035.0,59567035.0,2,1000000.0,Dr. No,1962-06-01,7.7,1962
3-
Sean Connery,11,Bentley,115,7.5,24800000.0,78900000.0,0,2000000.0,From Russia with Love,1963-08-01,8.0,1963
4-
Sean Connery,9,Aston Martin,110,7.8,51100000.0,124900000.0,1,3000000.0,Goldfinger,1964-05-01,8.4,1964
5-
Sean Connery,20,Aston Martin,130,7.0,63600000.0,141200000.0,0,9000000.0,Thunderball,1965-09-01,6.8,1965
6-
Sean Connery,21,Toyota,117,6.9,43100000.0,111600000.0,1,9500000.0,You Only Live Twice,1967-11-01,6.3,1967
7-
George Lazenby,5,Mercury,142,6.8,22800000.0,82000000.0,1,8000000.0,On Her Majesty's Secret Service,1969-07-01,6.7,1969
8-
Sean Connery,7,Ford,120,6.7,43800000.0,116000000.0,0,7200000.0,Diamonds Are Forever,1971-03-01,6.3,1971
9-
Roger Moore,8,AMC,121,6.8,35400000.0,161800000.0,0,7000000.0,Live and Let Die,1973-08-01,5.9,1973
10-
Roger Moore,1,AMC,125,6.7,21000000.0,97600000.0,0,7000000.0,The Man with the Golden Gun,1974-07-01,5.1,1974
11-
Roger Moore,31,Lotus,125,7.1,46800000.0,185400000.0,1,14000000.0,The Spy Who Loved Me,1977-04-01,6.8,1977
12-
Roger Moore,12,Lotus,126,6.2,70300000.0,210300000.0,1,31000000.0,Moonraker,1979-10-01,5.7,1979
13-
Roger Moore,18,Citroen,127,6.8,54800000.0,195300000.0,0,28000000.0,For Your Eyes Only,1981-06-01,6.3,1981
14-
Roger Moore,15,Bajaj,131,6.5,67900000.0,187500000.0,0,27500000.0,Octopussy,1983-03-01,5.3,1983
15-
Roger Moore,5,Rolls Royce,131,6.2,50327960.0,152627960.0,0,30000000.0,A View to a Kill,1985-10-01,4.7,1985
16-
Timothy Dalton,13,Rolls Royce,130,6.7,51185000.0,191200000.0,2,40000000.0,The Living Daylights,1987-05-01,6.3,1987
17-
Timothy Dalton,10,Aston Martin,133,6.5,34667015.0,156167015.0,1,42000000.0,License to Kill,1989-01-01,6.0,1989
18-
Pierce Brosnan,47,BMW,130,7.2,106429941.0,356429941.0,1,60000000.0,GoldenEye,1995-09-01,6.9,1995
19-
Pierce Brosnan,30,Aston Martin,119,6.4,125304276.0,339504276.0,1,110000000.0,Tomorrow Never Dies,1997-07-01,6.0,1997
20-
Pierce Brosnan,27,BMW,128,6.3,126930660.0,361730660.0,1,135000000.0,The World Is Not Enough,1999-06-01,5.7,1999
21-
Pierce Brosnan,31,Aston Martin,133,6.0,160942139.0,431942139.0,2,142000000.0,Die Another Day,2002-08-01,6.1,2002
22-
Daniel Craig,11,Aston Martin,144,7.9,167365000.0,596365000.0,3,102000000.0,Casino Royale,2006-02-01,7.8,2006
23-
Daniel Craig,16,Aston Martin,106,6.7,169368427.0,591692078.0,6,230000000.0,Quantum of Solace,2008-12-01,6.1,2008
24-
Daniel Craig,26,Aston Martin,143,7.8,304360277.0,1108561108.0,1,200000000.0,Skyfall,2012-11-01,8.2,2012
25-
Daniel Craig,30,Aston Martin,148,6.8,200074175.0,879620923.0,1,245000000.0,Spectre,2015-09-01,6.4,2015
26-
Daniel Craig,14,Aston Martin,163,7.3,160891007.0,759959662.0,1,275000000.0,No Time to Die,2021-11-01,7.3,2021
1+
release_date,movie_title,bond_actor,car_manufacturer,income_usa,income_world,movie_budget,film_length,imdb,rotten_tomatoes,martinis_consumed,bond_kills,release_year
2+
1962-06-01,Dr. No,Sean Connery,Sunbeam,16067035.0,59567035.0,1000000.0,110,7.3,7.7,2,4,1962
3+
1963-08-01,From Russia with Love,Sean Connery,Bentley,24800000.0,78900000.0,2000000.0,115,7.5,8.0,0,11,1963
4+
1964-05-01,Goldfinger,Sean Connery,Aston Martin,51100000.0,124900000.0,3000000.0,110,7.8,8.4,1,9,1964
5+
1965-09-01,Thunderball,Sean Connery,Aston Martin,63600000.0,141200000.0,9000000.0,130,7.0,6.8,0,20,1965
6+
1967-11-01,You Only Live Twice,Sean Connery,Toyota,43100000.0,111600000.0,9500000.0,117,6.9,6.3,1,21,1967
7+
1969-07-01,On Her Majesty's Secret Service,George Lazenby,Mercury,22800000.0,82000000.0,8000000.0,142,6.8,6.7,1,5,1969
8+
1971-03-01,Diamonds Are Forever,Sean Connery,Ford,43800000.0,116000000.0,7200000.0,120,6.7,6.3,0,7,1971
9+
1973-08-01,Live and Let Die,Roger Moore,AMC,35400000.0,161800000.0,7000000.0,121,6.8,5.9,0,8,1973
10+
1974-07-01,The Man with the Golden Gun,Roger Moore,AMC,21000000.0,97600000.0,7000000.0,125,6.7,5.1,0,1,1974
11+
1977-04-01,The Spy Who Loved Me,Roger Moore,Lotus,46800000.0,185400000.0,14000000.0,125,7.1,6.8,1,31,1977
12+
1979-10-01,Moonraker,Roger Moore,Lotus,70300000.0,210300000.0,31000000.0,126,6.2,5.7,1,12,1979
13+
1981-06-01,For Your Eyes Only,Roger Moore,Citroen,54800000.0,195300000.0,28000000.0,127,6.8,6.3,0,18,1981
14+
1983-03-01,Octopussy,Roger Moore,Bajaj,67900000.0,187500000.0,27500000.0,131,6.5,5.3,0,15,1983
15+
1985-10-01,A View to a Kill,Roger Moore,Rolls Royce,50327960.0,152627960.0,30000000.0,131,6.2,4.7,0,5,1985
16+
1987-05-01,The Living Daylights,Timothy Dalton,Rolls Royce,51185000.0,191200000.0,40000000.0,130,6.7,6.3,2,13,1987
17+
1989-01-01,License to Kill,Timothy Dalton,Aston Martin,34667015.0,156167015.0,42000000.0,133,6.5,6.0,1,10,1989
18+
1995-09-01,GoldenEye,Pierce Brosnan,BMW,106429941.0,356429941.0,60000000.0,130,7.2,6.9,1,47,1995
19+
1997-07-01,Tomorrow Never Dies,Pierce Brosnan,Aston Martin,125304276.0,339504276.0,110000000.0,119,6.4,6.0,1,30,1997
20+
1999-06-01,The World Is Not Enough,Pierce Brosnan,BMW,126930660.0,361730660.0,135000000.0,128,6.3,5.7,1,27,1999
21+
2002-08-01,Die Another Day,Pierce Brosnan,Aston Martin,160942139.0,431942139.0,142000000.0,133,6.0,6.1,2,31,2002
22+
2006-02-01,Casino Royale,Daniel Craig,Aston Martin,167365000.0,596365000.0,102000000.0,144,7.9,7.8,3,11,2006
23+
2008-12-01,Quantum of Solace,Daniel Craig,Aston Martin,169368427.0,591692078.0,230000000.0,106,6.7,6.1,6,16,2008
24+
2012-11-01,Skyfall,Daniel Craig,Aston Martin,304360277.0,1108561108.0,200000000.0,143,7.8,8.2,1,26,2012
25+
2015-09-01,Spectre,Daniel Craig,Aston Martin,200074175.0,879620923.0,245000000.0,148,6.8,6.4,1,30,2015
26+
2021-11-01,No Time to Die,Daniel Craig,Aston Martin,160891007.0,759959662.0,275000000.0,163,7.3,7.3,1,14,2021

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