Pandas/Pandas_Apply_custom_styles_on_column.ipynb

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   "source": [
    "# Pandas - Apply custom styles on column\n",
    "<a href=\"https://bit.ly/3JyWIk6\">Give Feedback</a> | <a href=\"https://github.com/jupyter-naas/awesome-notebooks/issues/new?assignees=&labels=bug&template=bug_report.md&title=Pandas+-+Apply+custom+styles+on+column:+Error+short+description\">Bug report</a>"
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   "source": [
    "**Tags:** #pandas #dataframe #style #column #apply #custom"
   ]
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   "source": [
    "**Author:** [Benjamin Filly](https://www.linkedin.com/in/benjamin-filly-05427727a/)"
   ]
  },
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   "source": [
    "**Last update:** 2023-07-25 (Created: 2023-07-24)"
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   "source": [
    "**Description:** This notebook will show how to apply custom styles on a column of a Pandas DataFrame. It is usefull for data analysis and data visualization."
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   "source": [
    "**References:**\n",
    "- [Pandas Documentation - Styling](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)\n",
    "- [Pandas Documentation - Styling with functions](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html#Styling-with-functions)"
   ]
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   "source": [
    "## Input"
   ]
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   "source": [
    "### Import libraries"
   ]
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   "source": [
    "import pandas as pd"
   ]
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   "source": [
    "## Model"
   ]
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   "source": [
    "### Create DataFrame"
   ]
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   "source": [
    "# Sample DataFrame\n",
    "data = {\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],\n",
    "    'Score': [95, -80, 70, -60, 85],\n",
    "    'Age': [25, 32, 18, 40, 28],\n",
    "    'Sales': [1200, 980, 1500, 850, 1750]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)"
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   "source": [
    "### Highlight negative\n",
    "In this example, we use a custom style function highlight_negative that applies the CSS attribute color: red to any cell in the `Score` column that contains a negative value. This will change the text color of negative values to red, making them stand out in the DataFrame."
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   "source": [
    "df_highlight_negative = df.copy() # Copying the dataframe df into another variable\n",
    "\n",
    "# Define a function that returns the CSS styles based on the values of the column\n",
    "def highlight_negative(s):\n",
    "    return ['color: red' if value < 0 else '' for value in s]\n",
    "\n",
    "df_highlight_negative = df_highlight_negative.style.apply(highlight_negative, subset=['Score'])\n",
    "df_highlight_negative"
   ]
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   "source": [
    "### Color scale\n",
    "The `color_scale` function applies a color scale to the `Age` column of the DataFrame. It assigns blue color to smaller values and red color to larger values, with varying intensity based on the values' magnitudes."
   ]
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    "df_color_scale = df.copy()\n",
    "\n",
    "def color_scale(s):\n",
    "    min_val = min(s)\n",
    "    max_val = max(s)\n",
    "    color_range = ['background-color: blue; opacity: 0.6',\n",
    "                   'background-color: rgb(255, 0, 0); opacity: 0.6']\n",
    "    normalized_values = (s - min_val) / (max_val - min_val)\n",
    "    color_mapped = pd.cut(normalized_values, bins=[-0.001, 0.5, 1], labels=color_range)\n",
    "    return color_mapped\n",
    "\n",
    "# Apply the custom styles to the 'Age' column\n",
    "df_color_scale = df_color_scale.style.apply(color_scale, subset=['Age'])\n",
    "\n",
    "# Display the styled DataFrame\n",
    "df_color_scale"
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   "source": [
    "### Highlight Max\n",
    "This style function is applied to the 'Sales' column, highlighting the cell with the maximum value using a purple background."
   ]
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   "source": [
    "df_highlight_max = df.copy()\n",
    "\n",
    "def highlight_max(s):\n",
    "    is_max = s == s.max()\n",
    "    return ['background-color: purple' if v else '' for v in is_max]\n",
    "\n",
    "df_highlight_max = df_highlight_max.style.apply(highlight_max, subset=['Sales'])\n",
    "df_highlight_max"
   ]
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   "source": [
    "## Output"
   ]
  },
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   "source": [
    "### Display result"
   ]
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    "# Apply the custom styles to different columns\n",
    "styled_df = df.style.apply(highlight_negative, subset=['Score']) \\\n",
    "                      .apply(color_scale, subset=['Age']) \\\n",
    "                      .apply(highlight_max, subset=['Sales']) # Put \"\\\" when you want to apply more style\n",
    "\n",
    "# Display the styled DataFrame\n",
    "styled_df"
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