Skip to content

Instantly share code, notes, and snippets.

@kshirsagarsiddharth

kshirsagarsiddharth/AreaPlots.ipynb

Created December 14, 2019 11:59
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save kshirsagarsiddharth/01b097acf6be89d3dadbce7a3ed4fb83 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save kshirsagarsiddharth/01b097acf6be89d3dadbce7a3ed4fb83 to your computer and use it in GitHub Desktop.
Download ZIP
Created on Cognitive Class Labs
Display the source blob
Display the rendered blob
Raw
AreaPlots.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Overlapped Area Plot for a DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
"#Peak temperature data for two cities\n",
"tempData = {\"City1\":[99,106,102,78],\n",
" \"City2\":[77,84,80,85]}\n",
"seasons = (\"Spring\",\"Summer\",\"Fall\",\"Winter\")\n",
"dataFrame = pd.DataFrame(tempData,index=seasons)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dataFrame.plot(kind='area',stacked=False)\n",
"plt.show(block=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Stacked area plot for the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
"#number of observations\n",
"data = [(25,1),(43,1),(35,2),(34,4)]"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [],
"source": [
"index=[\"2016\",\"2017\",\"2018\",\"2019\"]"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [],
"source": [
"columns = [\"Meteors\",\"Meteorites\"]"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Meteors</th>\n",
" <th>Meteorites</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>43</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018</th>\n",
" <td>35</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019</th>\n",
" <td>34</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Meteors Meteorites\n",
"2016 25 1\n",
"2017 43 1\n",
"2018 35 2\n",
"2019 34 4"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data=data,index=index,columns=columns)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot(kind=\"area\",stacked=True)\n",
"plt.show(block=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Percentage based Area Plot for a dataframe\n"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [],
"source": [
"gdpRevenue = {\"Agriculture\":(200,192,193),\n",
" \"Dairy\":(495,475,488),\n",
" \"Electronics\":(400,475,488),\n",
" \"Financial Services\":(200,220,230),\n",
" \"Others\":(150,155,170)}\n",
"index = (\"2010\",\"2011\",\"2012\")\n",
"dataFrame = pd.DataFrame(data=gdpRevenue)"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Agriculture</th>\n",
" <th>Dairy</th>\n",
" <th>Electronics</th>\n",
" <th>Financial Services</th>\n",
" <th>Others</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200</td>\n",
" <td>495</td>\n",
" <td>400</td>\n",
" <td>200</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>192</td>\n",
" <td>475</td>\n",
" <td>475</td>\n",
" <td>220</td>\n",
" <td>155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>193</td>\n",
" <td>488</td>\n",
" <td>488</td>\n",
" <td>230</td>\n",
" <td>170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Agriculture Dairy Electronics Financial Services Others\n",
"0 200 495 400 200 150\n",
"1 192 475 475 220 155\n",
"2 193 488 488 230 170"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataFrame"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn import preprocessing\n",
"\n",
"x = dataFrame.values \n",
"min_max_scaler = preprocessing.MinMaxScaler()\n",
"x_scaled = min_max_scaler.fit_transform(x)\n",
"normalized = pd.DataFrame(x_scaled)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [],
"source": [
"normalized.index = index\n",
"normalized.columns = [\"A\",\"b\",\"c\",\"d\",\"e\"]"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e0a57e80>"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"normalized.plot(kind=\"area\",stacked=False)"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [],
"source": [
"zonalRevenue = {\"East\":(25,27,32,31),\n",
" \"West\":(32,40,39,44),\n",
" \"South\":(34,31,32,34),\n",
" \"North\":(27,26,22,28)}\n",
"years = (\"2016\",\"2017\",\"2018\",\"2019\")"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [],
"source": [
"dataFrame = pd.DataFrame(data=zonalRevenue)"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [],
"source": [
"columns = dataFrame.columns"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [],
"source": [
"min_max_scaler = preprocessing.MinMaxScaler()\n",
"standard_scaler = preprocessing.StandardScaler()\n",
"pieceofshit_normalization = dataFrame.div(dataFrame.sum(axis=1),axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n",
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"min_max_scaler_normalized = min_max_scaler.fit_transform(dataFrame.values)\n",
"standard_scaler_normalized = standard_scaler.fit_transform(dataFrame.values)"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [],
"source": [
"pieceofshit_normalization.index =years"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [],
"source": [
"min_max_scaler_normalized = pd.DataFrame(min_max_scaler_normalized)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [],
"source": [
"min_max_scaler_normalized.columns = dataFrame.columns\n"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [],
"source": [
"min_max_scaler_normalized.index = years"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [],
"source": [
"standard_scaler_normalized = pd.DataFrame(standard_scaler_normalized)"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [],
"source": [
"standard_scaler_normalized.columns = dataFrame.columns"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [],
"source": [
"standard_scaler_normalized.index = years"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e08c5668>"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pieceofshit_normalization.plot(kind=\"area\",stacked=False)"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>East</th>\n",
" <th>West</th>\n",
" <th>South</th>\n",
" <th>North</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017</th>\n",
" <td>0.211864</td>\n",
" <td>0.271186</td>\n",
" <td>0.288136</td>\n",
" <td>0.228814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018</th>\n",
" <td>0.217742</td>\n",
" <td>0.322581</td>\n",
" <td>0.250000</td>\n",
" <td>0.209677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019</th>\n",
" <td>0.256000</td>\n",
" <td>0.312000</td>\n",
" <td>0.256000</td>\n",
" <td>0.176000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>0.226277</td>\n",
" <td>0.321168</td>\n",
" <td>0.248175</td>\n",
" <td>0.204380</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" East West South North\n",
"2017 0.211864 0.271186 0.288136 0.228814\n",
"2018 0.217742 0.322581 0.250000 0.209677\n",
"2019 0.256000 0.312000 0.256000 0.176000\n",
"2016 0.226277 0.321168 0.248175 0.204380"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pieceofshit_normalization"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e0849208>"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_max_scaler_normalized.plot(kind='area',stacked=False)"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "When stacked is True, each column must be either all positive or negative.East contains both positive and negative values",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-194-c45fc13d051c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstandard_scaler_normalized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'line'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstacked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 792\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 796\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left_ax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 282\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1077\u001b[0m \u001b[0mstacking_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacking_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[0mis_errorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_errorbar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1079\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1080\u001b[0m )\n\u001b[1;32m 1081\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewlines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[1;32m 1091\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumn_num\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1092\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize_stacker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacking_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m \u001b[0my_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_stacked_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacking_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"label\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1094\u001b[0m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMPLPlot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1095\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_stacker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacking_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_get_stacked_values\u001b[0;34m(cls, ax, stacking_id, values, label)\u001b[0m\n\u001b[1;32m 1156\u001b[0m \u001b[0;34m\"When stacked is True, each column must be either \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1157\u001b[0m \u001b[0;34m\"all positive or negative.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1158\u001b[0;31m \u001b[0;34m\"{0} contains both positive and negative values\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1159\u001b[0m )\n\u001b[1;32m 1160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: When stacked is True, each column must be either all positive or negative.East contains both positive and negative values"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"standard_scaler_normalized.plot(kind='line',stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment