graph.ipynb

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   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/tmp/ipykernel_18874/3134535709.py:2: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df_children = pd.read_csv(\"HIV_Children_2023.csv\")\n"
     ]
    }
   ],
   "source": [
    "df_testing = pd.read_csv(\"HIV_ANC_Testing_2023.csv\")\n",
    "df_children = pd.read_csv(\"HIV_Children_2023.csv\")\n",
    "df_infant = pd.read_csv(\"HIV_Early_Infant_Diagnosis_2023.csv\")\n",
    "df_paediatric = pd.read_csv(\"HIV_Paediatric_2023.csv\")\n",
    "df_pmtct = pd.read_csv(\"HIV_PMTCT_2023.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2015, 2017, 2018, 2019, 2020, 2021, 2022])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_testing.Year.unique()"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021,\n",
       "       2022, 2010])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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  {
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    "df_ = df_infant[df_infant['Indicator'] == 'Reported number of infants born to pregnant women living with HIV who received a virological test for HIV within 2 months of birth']\n",
    "df_ = df_[df_['Year'] == 2022]\n",
    "px.choropleth(df_, locations='ISO3',color='Value')"
   ]
  },
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   "source": [
    "df_ = df_children[df_children['Country/Region']=='Global']\n",
    "df_ = df_[df_['Indicator']=='Estimated incidence rate (new HIV infection per 1,000 uninfected population)']\n",
    "df_ = df_[df_['Sex'] == 'Both']\n",
    "df_ = df_[df_['Age'] == 'Age 0-14']\n",
    "px.line(df_,x='Year',y=['Value','Lower','Upper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 23 entries, 0 to 132\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   ISO3            23 non-null     object\n",
      " 1   Type            23 non-null     object\n",
      " 2   Country/Region  23 non-null     object\n",
      " 3   UNICEF Region   0 non-null      object\n",
      " 4   Indicator       23 non-null     object\n",
      " 5   Data source     23 non-null     object\n",
      " 6   Year            23 non-null     int64 \n",
      " 7   Sex             23 non-null     object\n",
      " 8   Age             23 non-null     object\n",
      " 9   Value           23 non-null     object\n",
      " 10  Lower           23 non-null     object\n",
      " 11  Upper           23 non-null     object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 2.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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