pickle_&_csv_reading.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8ea3c4c",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56e4e091",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b5da237",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To install pickle:\n",
    "# !pip install pickle5\n",
    "# To install pandas:\n",
    "# !pip install pandas\n",
    "# To install numpy:\n",
    "# !pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "708371fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For pickle files:\n",
    "import pickle\n",
    "# For csv files\n",
    "import pandas as pd\n",
    "# For arrays\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a390b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to install epiweeks in python:\n",
    "# !pip install epiweeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf4e1f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Epiweek:\n",
    "from epiweeks import Week, Year\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa27d965",
   "metadata": {},
   "source": [
    "# 1. Read pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69f301f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to read file:\n",
    "def read_pickle(file_path):\n",
    "    infile = open(file_path,'rb')\n",
    "    file = pickle.load(infile)\n",
    "    infile.close()\n",
    "    print(f'The file has a shape: {file.shape} and class: {type(file)}')\n",
    "    return file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6ca185f",
   "metadata": {},
   "source": [
    "## Examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e462f574",
   "metadata": {},
   "source": [
    "### Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52da5e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file has a shape: (164, 100) and class: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Path to picke file:\n",
    "# file_path = 'path_to_pickle.pkl'\n",
    "file_path = 'Embeddings/features_resnet50.pkl'\n",
    "\n",
    "# Read the file:\n",
    "file = read_pickle(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfff19de",
   "metadata": {},
   "source": [
    "### Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b33d4293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file has a shape: (164, 100) and class: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Path to picke file:\n",
    "# file_path = 'path_to_pickle.pkl'\n",
    "file_path = 'Embeddings/features_transformer.pkl'\n",
    "\n",
    "# Read the file:\n",
    "file = read_pickle(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5a6075",
   "metadata": {},
   "source": [
    "## Numpy array to pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1da040ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to split one column of features into multiple columns:\n",
    "def split_columns(df):\n",
    "    df_aux = pd.DataFrame(df['features'].tolist())\n",
    "    df_aux = pd.concat( [df['Date'], df_aux], axis=1)\n",
    "    return df_aux\n",
    "\n",
    "\n",
    "# Function to genearte the dataframe from features and date arrays:\n",
    "def generate_dataframe(features, dates):\n",
    "    features_df = pd.DataFrame(columns=['Date', 'features'])\n",
    "    for i in range(len(features)):\n",
    "        image_features = features[i]\n",
    "        date = dates[i]\n",
    "        features_df = features_df.append({'Date': date, 'features': image_features}, ignore_index=True )\n",
    "    # new df from the column of lists\n",
    "    features_df = split_columns(features_df)\n",
    "    return features_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd6e9fb",
   "metadata": {},
   "source": [
    "## Examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf98fca",
   "metadata": {},
   "source": [
    "### Example 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98399c64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file has a shape: (165,) and class: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Read the date:\n",
    "\n",
    "# Path to date\n",
    "dates_path = 'Embeddings/dates.pkl'\n",
    "# Read the file:\n",
    "dates = read_pickle(dates_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a11210e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file has a shape: (165, 100) and class: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Read the features:\n",
    "\n",
    "# Path to picke file:\n",
    "file_path = 'Embeddings/features100_vae.pkl'\n",
    "# Read the file:\n",
    "file = read_pickle(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31b0b1f0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.408913</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015-11-08</td>\n",
       "      <td>-0.375792</td>\n",
       "      <td>1.282191</td>\n",
       "      <td>-1.769311</td>\n",
       "      <td>-1.768404</td>\n",
       "      <td>0.960811</td>\n",
       "      <td>-1.401841</td>\n",
       "      <td>-0.138934</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>-1.184208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427782</td>\n",
       "      <td>-2.927658</td>\n",
       "      <td>0.504012</td>\n",
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       "      <td>-1.147623</td>\n",
       "      <td>-2.653756</td>\n",
       "      <td>2.155063</td>\n",
       "      <td>-1.433320</td>\n",
       "      <td>-1.319588</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-15</td>\n",
       "      <td>0.270060</td>\n",
       "      <td>1.896669</td>\n",
       "      <td>0.160804</td>\n",
       "      <td>0.100467</td>\n",
       "      <td>-1.593640</td>\n",
       "      <td>-0.673158</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>1.709716</td>\n",
       "      <td>-0.976310</td>\n",
       "      <td>...</td>\n",
       "      <td>1.151502</td>\n",
       "      <td>-1.066319</td>\n",
       "      <td>0.433531</td>\n",
       "      <td>0.623669</td>\n",
       "      <td>1.678527</td>\n",
       "      <td>-2.044615</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>1.307580</td>\n",
       "      <td>-0.059874</td>\n",
       "      <td>-0.276768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>-1.050474</td>\n",
       "      <td>-0.492467</td>\n",
       "      <td>-3.057118</td>\n",
       "      <td>-2.102062</td>\n",
       "      <td>0.290074</td>\n",
       "      <td>-1.837421</td>\n",
       "      <td>0.771637</td>\n",
       "      <td>0.883183</td>\n",
       "      <td>0.827531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586739</td>\n",
       "      <td>-0.414671</td>\n",
       "      <td>-0.480104</td>\n",
       "      <td>-1.629912</td>\n",
       "      <td>3.934243</td>\n",
       "      <td>2.386098</td>\n",
       "      <td>-1.041708</td>\n",
       "      <td>0.388762</td>\n",
       "      <td>0.855838</td>\n",
       "      <td>-1.768747</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>0.277096</td>\n",
       "      <td>-0.177303</td>\n",
       "      <td>1.520242</td>\n",
       "      <td>1.147505</td>\n",
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       "      <td>1.057434</td>\n",
       "      <td>1.925118</td>\n",
       "      <td>0.395960</td>\n",
       "      <td>...</td>\n",
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       "      <td>-1.540928</td>\n",
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       "<p>5 rows × 101 columns</p>\n",
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       "         Date         0         1         2         3         4         5  \\\n",
       "0  2015-11-01 -0.773164  0.409429 -1.967424 -0.194265  1.589418 -0.806473   \n",
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       "2  2015-11-15  0.270060  1.896669  0.160804  0.100467 -1.593640 -0.673158   \n",
       "3  2015-11-22 -1.050474 -0.492467 -3.057118 -2.102062  0.290074 -1.837421   \n",
       "4  2015-11-29  0.277096 -0.177303  1.520242  1.147505  0.999619  0.343843   \n",
       "\n",
       "          6         7         8  ...        90        91        92        93  \\\n",
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       "1 -0.138934  0.260522 -1.184208  ...  1.427782 -2.927658  0.504012 -0.062580   \n",
       "2  0.158351  1.709716 -0.976310  ...  1.151502 -1.066319  0.433531  0.623669   \n",
       "3  0.771637  0.883183  0.827531  ...  1.586739 -0.414671 -0.480104 -1.629912   \n",
       "4  1.057434  1.925118  0.395960  ...  1.060054 -0.819456 -0.657092  0.556108   \n",
       "\n",
       "         94        95        96        97        98        99  \n",
       "0 -1.408913  2.835881 -4.650385  2.829885 -3.892387 -2.027068  \n",
       "1  3.175248 -1.147623 -2.653756  2.155063 -1.433320 -1.319588  \n",
       "2  1.678527 -2.044615  0.735826  1.307580 -0.059874 -0.276768  \n",
       "3  3.934243  2.386098 -1.041708  0.388762  0.855838 -1.768747  \n",
       "4  3.519002 -1.769988 -2.901545  0.582610  3.461969 -1.540928  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genearate dataframe\n",
    "features_df = generate_dataframe(file, dates)\n",
    "\n",
    "# Show features as dataframe:\n",
    "features_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a139fbb",
   "metadata": {},
   "source": [
    "# 2. Read csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e372f6fc",
   "metadata": {},
   "source": [
    "## Examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4df7a2",
   "metadata": {},
   "source": [
    "### Example 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a16da8ed",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>2015-11-08</td>\n",
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       "      <th>2</th>\n",
       "      <td>2015-11-15</td>\n",
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       "      <td>-28.032221</td>\n",
       "      <td>-37.096690</td>\n",
       "      <td>-3.305299</td>\n",
       "      <td>-67.746083</td>\n",
       "      <td>37.584909</td>\n",
       "      <td>-11.838416</td>\n",
       "      <td>-15.613838</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.746142</td>\n",
       "      <td>13.683615</td>\n",
       "      <td>-1.131025</td>\n",
       "      <td>-3.349845</td>\n",
       "      <td>-10.681103</td>\n",
       "      <td>-6.437450</td>\n",
       "      <td>12.024817</td>\n",
       "      <td>12.829025</td>\n",
       "      <td>12.699458</td>\n",
       "      <td>0.475610</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>-149.578506</td>\n",
       "      <td>118.675591</td>\n",
       "      <td>-28.032221</td>\n",
       "      <td>-37.096690</td>\n",
       "      <td>-3.305299</td>\n",
       "      <td>-67.746083</td>\n",
       "      <td>37.584909</td>\n",
       "      <td>-11.838416</td>\n",
       "      <td>-15.613838</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.746142</td>\n",
       "      <td>13.683615</td>\n",
       "      <td>-1.131025</td>\n",
       "      <td>-3.349845</td>\n",
       "      <td>-10.681103</td>\n",
       "      <td>-6.437450</td>\n",
       "      <td>12.024817</td>\n",
       "      <td>12.829025</td>\n",
       "      <td>12.699458</td>\n",
       "      <td>0.475610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>-28.099436</td>\n",
       "      <td>34.715737</td>\n",
       "      <td>47.153387</td>\n",
       "      <td>-50.761985</td>\n",
       "      <td>-63.137078</td>\n",
       "      <td>-58.088638</td>\n",
       "      <td>-6.508121</td>\n",
       "      <td>18.149721</td>\n",
       "      <td>-29.781310</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.670381</td>\n",
       "      <td>-5.520467</td>\n",
       "      <td>-4.101447</td>\n",
       "      <td>1.232668</td>\n",
       "      <td>-2.903102</td>\n",
       "      <td>2.551955</td>\n",
       "      <td>-3.338012</td>\n",
       "      <td>-5.490846</td>\n",
       "      <td>-2.383766</td>\n",
       "      <td>-1.712792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date           0           1          2          3          4  \\\n",
       "0  2015-11-01   98.435754  108.962228 -40.915729  39.468939  47.325778   \n",
       "1  2015-11-08   98.435754  108.962228 -40.915729  39.468939  47.325778   \n",
       "2  2015-11-15 -149.578506  118.675591 -28.032221 -37.096690  -3.305299   \n",
       "3  2015-11-22 -149.578506  118.675591 -28.032221 -37.096690  -3.305299   \n",
       "4  2015-11-29  -28.099436   34.715737  47.153387 -50.761985 -63.137078   \n",
       "\n",
       "           5           6           7          8  ...         90         91  \\\n",
       "0  70.472168  187.431726 -229.801740  43.888213  ...   0.587965  -0.365837   \n",
       "1  70.472168  187.431726 -229.801740  43.888213  ...   0.587965  -0.365837   \n",
       "2 -67.746083   37.584909  -11.838416 -15.613838  ... -13.746142  13.683615   \n",
       "3 -67.746083   37.584909  -11.838416 -15.613838  ... -13.746142  13.683615   \n",
       "4 -58.088638   -6.508121   18.149721 -29.781310  ...  -6.670381  -5.520467   \n",
       "\n",
       "         92        93         94        95         96         97         98  \\\n",
       "0 -2.923909  0.096117  -0.608161  0.119047   1.095555  -1.305447  -1.034465   \n",
       "1 -2.923909  0.096117  -0.608161  0.119047   1.095555  -1.305447  -1.034465   \n",
       "2 -1.131025 -3.349845 -10.681103 -6.437450  12.024817  12.829025  12.699458   \n",
       "3 -1.131025 -3.349845 -10.681103 -6.437450  12.024817  12.829025  12.699458   \n",
       "4 -4.101447  1.232668  -2.903102  2.551955  -3.338012  -5.490846  -2.383766   \n",
       "\n",
       "         99  \n",
       "0 -0.486601  \n",
       "1 -0.486601  \n",
       "2  0.475610  \n",
       "3  0.475610  \n",
       "4 -1.712792  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Path to csv file:\n",
    "# file_path = 'path_to_csv.csv'\n",
    "file_path = 'Embeddings/pca_medellin_100features.csv'\n",
    "\n",
    "# Read csv file\n",
    "file = pd.read_csv(file_path)\n",
    "\n",
    "# Show the dataframe:\n",
    "# Number of rows to show:\n",
    "rows = 5 \n",
    "# show:\n",
    "file.head(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ff75d6",
   "metadata": {},
   "source": [
    "### Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02c87878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Date</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>-0.773164</td>\n",
       "      <td>0.409429</td>\n",
       "      <td>-1.967424</td>\n",
       "      <td>-0.194265</td>\n",
       "      <td>1.589418</td>\n",
       "      <td>-0.806473</td>\n",
       "      <td>3.013131</td>\n",
       "      <td>-0.131107</td>\n",
       "      <td>-1.274766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.858297</td>\n",
       "      <td>-1.143137</td>\n",
       "      <td>-0.012872</td>\n",
       "      <td>2.545450</td>\n",
       "      <td>-1.408913</td>\n",
       "      <td>2.835881</td>\n",
       "      <td>-4.650385</td>\n",
       "      <td>2.829885</td>\n",
       "      <td>-3.892387</td>\n",
       "      <td>-2.027068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-08</td>\n",
       "      <td>-0.375792</td>\n",
       "      <td>1.282191</td>\n",
       "      <td>-1.769311</td>\n",
       "      <td>-1.768404</td>\n",
       "      <td>0.960811</td>\n",
       "      <td>-1.401841</td>\n",
       "      <td>-0.138934</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>-1.184208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427782</td>\n",
       "      <td>-2.927658</td>\n",
       "      <td>0.504012</td>\n",
       "      <td>-0.062580</td>\n",
       "      <td>3.175248</td>\n",
       "      <td>-1.147623</td>\n",
       "      <td>-2.653756</td>\n",
       "      <td>2.155063</td>\n",
       "      <td>-1.433320</td>\n",
       "      <td>-1.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-15</td>\n",
       "      <td>0.270060</td>\n",
       "      <td>1.896669</td>\n",
       "      <td>0.160804</td>\n",
       "      <td>0.100467</td>\n",
       "      <td>-1.593640</td>\n",
       "      <td>-0.673158</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>1.709716</td>\n",
       "      <td>-0.976310</td>\n",
       "      <td>...</td>\n",
       "      <td>1.151502</td>\n",
       "      <td>-1.066319</td>\n",
       "      <td>0.433531</td>\n",
       "      <td>0.623669</td>\n",
       "      <td>1.678527</td>\n",
       "      <td>-2.044615</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>1.307580</td>\n",
       "      <td>-0.059874</td>\n",
       "      <td>-0.276768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>-1.050474</td>\n",
       "      <td>-0.492467</td>\n",
       "      <td>-3.057118</td>\n",
       "      <td>-2.102062</td>\n",
       "      <td>0.290074</td>\n",
       "      <td>-1.837421</td>\n",
       "      <td>0.771637</td>\n",
       "      <td>0.883183</td>\n",
       "      <td>0.827531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586739</td>\n",
       "      <td>-0.414671</td>\n",
       "      <td>-0.480104</td>\n",
       "      <td>-1.629912</td>\n",
       "      <td>3.934243</td>\n",
       "      <td>2.386098</td>\n",
       "      <td>-1.041708</td>\n",
       "      <td>0.388762</td>\n",
       "      <td>0.855838</td>\n",
       "      <td>-1.768747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>0.277096</td>\n",
       "      <td>-0.177303</td>\n",
       "      <td>1.520242</td>\n",
       "      <td>1.147505</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.343843</td>\n",
       "      <td>1.057434</td>\n",
       "      <td>1.925118</td>\n",
       "      <td>0.395960</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060054</td>\n",
       "      <td>-0.819456</td>\n",
       "      <td>-0.657092</td>\n",
       "      <td>0.556108</td>\n",
       "      <td>3.519002</td>\n",
       "      <td>-1.769988</td>\n",
       "      <td>-2.901545</td>\n",
       "      <td>0.582610</td>\n",
       "      <td>3.461969</td>\n",
       "      <td>-1.540928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date         0         1         2         3         4         5  \\\n",
       "0  2015-11-01 -0.773164  0.409429 -1.967424 -0.194265  1.589418 -0.806473   \n",
       "1  2015-11-08 -0.375792  1.282191 -1.769311 -1.768404  0.960811 -1.401841   \n",
       "2  2015-11-15  0.270060  1.896669  0.160804  0.100467 -1.593640 -0.673158   \n",
       "3  2015-11-22 -1.050474 -0.492467 -3.057118 -2.102062  0.290074 -1.837421   \n",
       "4  2015-11-29  0.277096 -0.177303  1.520242  1.147505  0.999619  0.343843   \n",
       "\n",
       "          6         7         8  ...        90        91        92        93  \\\n",
       "0  3.013131 -0.131107 -1.274766  ...  1.858297 -1.143137 -0.012872  2.545450   \n",
       "1 -0.138934  0.260522 -1.184208  ...  1.427782 -2.927658  0.504012 -0.062580   \n",
       "2  0.158351  1.709716 -0.976310  ...  1.151502 -1.066319  0.433531  0.623669   \n",
       "3  0.771637  0.883183  0.827531  ...  1.586739 -0.414671 -0.480104 -1.629912   \n",
       "4  1.057434  1.925118  0.395960  ...  1.060054 -0.819456 -0.657092  0.556108   \n",
       "\n",
       "         94        95        96        97        98        99  \n",
       "0 -1.408913  2.835881 -4.650385  2.829885 -3.892387 -2.027068  \n",
       "1  3.175248 -1.147623 -2.653756  2.155063 -1.433320 -1.319588  \n",
       "2  1.678527 -2.044615  0.735826  1.307580 -0.059874 -0.276768  \n",
       "3  3.934243  2.386098 -1.041708  0.388762  0.855838 -1.768747  \n",
       "4  3.519002 -1.769988 -2.901545  0.582610  3.461969 -1.540928  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Path to csv file:\n",
    "# file_path = 'path_to_csv.csv'\n",
    "file_path = 'Embeddings/embeddings_medellin_100features.csv'\n",
    "\n",
    "# Read csv file\n",
    "file = pd.read_csv(file_path)\n",
    "\n",
    "# Show the dataframe:\n",
    "# Number of rows to show:\n",
    "rows = 5 \n",
    "# show:\n",
    "file.head(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7cb3a2",
   "metadata": {},
   "source": [
    "## CSV file to numpy array and dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aca348ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to generate:\n",
    "# 1. Numpy array of features\n",
    "# 2. Dictionary of key = date and value = features of image the in that date:\n",
    "def get_embeddings_array(df):\n",
    "    embeddings_list = np.array(df.iloc[:,1:])\n",
    "    embeddings_date = np.array(df.iloc[:,0])\n",
    "    embeddings_dict = dict()\n",
    "    for date, embedding in zip(embeddings_date, embeddings_list):\n",
    "        embeddings_dict[date] = embedding\n",
    "        \n",
    "    print(f'Array of shape: {embeddings_list.shape} and class: {type(embeddings_list)} generated!')\n",
    "    print(f'Dictionary of class: {type(embeddings_dict)}, with keys = dates and values = features generated!')\n",
    "    return embeddings_dict, embeddings_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e7c21f",
   "metadata": {},
   "source": [
    "## Examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c286bd",
   "metadata": {},
   "source": [
    "### Example 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "572b8a66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array of shape: (165, 100) and class: <class 'numpy.ndarray'> generated!\n",
      "Dictionary of class: <class 'dict'>, with keys = dates and values = features generated!\n"
     ]
    }
   ],
   "source": [
    "# Path to csv file:\n",
    "file_path = 'Embeddings/pca_medellin_100features.csv'\n",
    "# Read csv file\n",
    "features_df = pd.read_csv(file_path)\n",
    "\n",
    "features_dictionary, features_array = get_embeddings_array(features_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40c3807",
   "metadata": {},
   "source": [
    "### Example 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5e3a1a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array of shape: (165, 200) and class: <class 'numpy.ndarray'> generated!\n",
      "Dictionary of class: <class 'dict'>, with keys = dates and values = features generated!\n"
     ]
    }
   ],
   "source": [
    "# Path to csv file:\n",
    "file_path = 'Embeddings/embeddings_medellin_200features.csv'\n",
    "# Read csv file\n",
    "features_df = pd.read_csv(file_path)\n",
    "\n",
    "features_dictionary, features_array = get_embeddings_array(features_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d767b2",
   "metadata": {},
   "source": [
    "# 3. Convert dates to epiweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "716babc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getEpiweek(date_str):\n",
    "    date_ls = date_str.split('-')\n",
    "    week_date = date(int(date_ls[0]), int(date_ls[1]), int(date_ls[2]))\n",
    "    week = Week.fromdate(week_date)\n",
    "    return week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ec0163c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dates_to_epiweek(dates_array):\n",
    "    epiweeks_array = list()\n",
    "    for date in dates_array:\n",
    "        epiweek = getEpiweek(date)\n",
    "        epiweeks_array.append(epiweek)\n",
    "    epiweeks_array = np.array(epiweeks_array)\n",
    "    print(f'The array with epiweeks of shape: {epiweeks_array.shape} and class: {type(epiweeks_array)} was generated!')\n",
    "    return epiweeks_array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2058b52",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0761a72a",
   "metadata": {},
   "source": [
    "### Example 1 \n",
    "###### Part 1: Using the pickle file with dates to generate an epiweek array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d48e796f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file has a shape: (165,) and class: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Read the date:\n",
    "\n",
    "# Path to date\n",
    "dates_path = 'Embeddings/dates.pkl'\n",
    "# Read the file:\n",
    "dates = read_pickle(dates_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e49df4cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The array with epiweeks of shape: (165,) and class: <class 'numpy.ndarray'> was generated!\n"
     ]
    }
   ],
   "source": [
    "# Convert dates to epiweeks\n",
    "epiweeks_array = dates_to_epiweek(dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a90621",
   "metadata": {},
   "source": [
    "### Example 1 \n",
    "\n",
    "###### Part 2: Using the array of epiweeks create to generate a dataframe with features and epiweek:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9bb8f366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file has a shape: (165, 100) and class: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Read the features:\n",
    "\n",
    "# Path to picke file:\n",
    "file_path = 'Embeddings/features100_vae.pkl'\n",
    "# Read the file:\n",
    "file = read_pickle(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "630df0eb",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201544</td>\n",
       "      <td>-0.773164</td>\n",
       "      <td>0.409429</td>\n",
       "      <td>-1.967424</td>\n",
       "      <td>-0.194265</td>\n",
       "      <td>1.589418</td>\n",
       "      <td>-0.806473</td>\n",
       "      <td>3.013131</td>\n",
       "      <td>-0.131107</td>\n",
       "      <td>-1.274766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.858297</td>\n",
       "      <td>-1.143137</td>\n",
       "      <td>-0.012872</td>\n",
       "      <td>2.545450</td>\n",
       "      <td>-1.408913</td>\n",
       "      <td>2.835881</td>\n",
       "      <td>-4.650385</td>\n",
       "      <td>2.829885</td>\n",
       "      <td>-3.892387</td>\n",
       "      <td>-2.027068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201545</td>\n",
       "      <td>-0.375792</td>\n",
       "      <td>1.282191</td>\n",
       "      <td>-1.769311</td>\n",
       "      <td>-1.768404</td>\n",
       "      <td>0.960811</td>\n",
       "      <td>-1.401841</td>\n",
       "      <td>-0.138934</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>-1.184208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427782</td>\n",
       "      <td>-2.927658</td>\n",
       "      <td>0.504012</td>\n",
       "      <td>-0.062580</td>\n",
       "      <td>3.175248</td>\n",
       "      <td>-1.147623</td>\n",
       "      <td>-2.653756</td>\n",
       "      <td>2.155063</td>\n",
       "      <td>-1.433320</td>\n",
       "      <td>-1.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201546</td>\n",
       "      <td>0.270060</td>\n",
       "      <td>1.896669</td>\n",
       "      <td>0.160804</td>\n",
       "      <td>0.100467</td>\n",
       "      <td>-1.593640</td>\n",
       "      <td>-0.673158</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>1.709716</td>\n",
       "      <td>-0.976310</td>\n",
       "      <td>...</td>\n",
       "      <td>1.151502</td>\n",
       "      <td>-1.066319</td>\n",
       "      <td>0.433531</td>\n",
       "      <td>0.623669</td>\n",
       "      <td>1.678527</td>\n",
       "      <td>-2.044615</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>1.307580</td>\n",
       "      <td>-0.059874</td>\n",
       "      <td>-0.276768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201547</td>\n",
       "      <td>-1.050474</td>\n",
       "      <td>-0.492467</td>\n",
       "      <td>-3.057118</td>\n",
       "      <td>-2.102062</td>\n",
       "      <td>0.290074</td>\n",
       "      <td>-1.837421</td>\n",
       "      <td>0.771637</td>\n",
       "      <td>0.883183</td>\n",
       "      <td>0.827531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586739</td>\n",
       "      <td>-0.414671</td>\n",
       "      <td>-0.480104</td>\n",
       "      <td>-1.629912</td>\n",
       "      <td>3.934243</td>\n",
       "      <td>2.386098</td>\n",
       "      <td>-1.041708</td>\n",
       "      <td>0.388762</td>\n",
       "      <td>0.855838</td>\n",
       "      <td>-1.768747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201548</td>\n",
       "      <td>0.277096</td>\n",
       "      <td>-0.177303</td>\n",
       "      <td>1.520242</td>\n",
       "      <td>1.147505</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.343843</td>\n",
       "      <td>1.057434</td>\n",
       "      <td>1.925118</td>\n",
       "      <td>0.395960</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060054</td>\n",
       "      <td>-0.819456</td>\n",
       "      <td>-0.657092</td>\n",
       "      <td>0.556108</td>\n",
       "      <td>3.519002</td>\n",
       "      <td>-1.769988</td>\n",
       "      <td>-2.901545</td>\n",
       "      <td>0.582610</td>\n",
       "      <td>3.461969</td>\n",
       "      <td>-1.540928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date         0         1         2         3         4         5  \\\n",
       "0  201544 -0.773164  0.409429 -1.967424 -0.194265  1.589418 -0.806473   \n",
       "1  201545 -0.375792  1.282191 -1.769311 -1.768404  0.960811 -1.401841   \n",
       "2  201546  0.270060  1.896669  0.160804  0.100467 -1.593640 -0.673158   \n",
       "3  201547 -1.050474 -0.492467 -3.057118 -2.102062  0.290074 -1.837421   \n",
       "4  201548  0.277096 -0.177303  1.520242  1.147505  0.999619  0.343843   \n",
       "\n",
       "          6         7         8  ...        90        91        92        93  \\\n",
       "0  3.013131 -0.131107 -1.274766  ...  1.858297 -1.143137 -0.012872  2.545450   \n",
       "1 -0.138934  0.260522 -1.184208  ...  1.427782 -2.927658  0.504012 -0.062580   \n",
       "2  0.158351  1.709716 -0.976310  ...  1.151502 -1.066319  0.433531  0.623669   \n",
       "3  0.771637  0.883183  0.827531  ...  1.586739 -0.414671 -0.480104 -1.629912   \n",
       "4  1.057434  1.925118  0.395960  ...  1.060054 -0.819456 -0.657092  0.556108   \n",
       "\n",
       "         94        95        96        97        98        99  \n",
       "0 -1.408913  2.835881 -4.650385  2.829885 -3.892387 -2.027068  \n",
       "1  3.175248 -1.147623 -2.653756  2.155063 -1.433320 -1.319588  \n",
       "2  1.678527 -2.044615  0.735826  1.307580 -0.059874 -0.276768  \n",
       "3  3.934243  2.386098 -1.041708  0.388762  0.855838 -1.768747  \n",
       "4  3.519002 -1.769988 -2.901545  0.582610  3.461969 -1.540928  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genearate dataframe\n",
    "features_df = generate_dataframe(file, epiweeks_array)\n",
    "\n",
    "# Show features as dataframe:\n",
    "features_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa734cf",
   "metadata": {},
   "source": [
    "### Example 2 \n",
    "###### Convert the column date from a csv file to epiweeks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "71e8bb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "      <th>2</th>\n",
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       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>-0.773164</td>\n",
       "      <td>0.409429</td>\n",
       "      <td>-1.967424</td>\n",
       "      <td>-0.194265</td>\n",
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       "      <td>-0.131107</td>\n",
       "      <td>-1.274766</td>\n",
       "      <td>...</td>\n",
       "      <td>1.858297</td>\n",
       "      <td>-1.143137</td>\n",
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       "      <td>-1.408913</td>\n",
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       "      <td>2.829885</td>\n",
       "      <td>-3.892387</td>\n",
       "      <td>-2.027068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-08</td>\n",
       "      <td>-0.375792</td>\n",
       "      <td>1.282191</td>\n",
       "      <td>-1.769311</td>\n",
       "      <td>-1.768404</td>\n",
       "      <td>0.960811</td>\n",
       "      <td>-1.401841</td>\n",
       "      <td>-0.138934</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>-1.184208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427782</td>\n",
       "      <td>-2.927658</td>\n",
       "      <td>0.504012</td>\n",
       "      <td>-0.062580</td>\n",
       "      <td>3.175248</td>\n",
       "      <td>-1.147623</td>\n",
       "      <td>-2.653756</td>\n",
       "      <td>2.155063</td>\n",
       "      <td>-1.433320</td>\n",
       "      <td>-1.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-15</td>\n",
       "      <td>0.270060</td>\n",
       "      <td>1.896669</td>\n",
       "      <td>0.160804</td>\n",
       "      <td>0.100467</td>\n",
       "      <td>-1.593640</td>\n",
       "      <td>-0.673158</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>1.709716</td>\n",
       "      <td>-0.976310</td>\n",
       "      <td>...</td>\n",
       "      <td>1.151502</td>\n",
       "      <td>-1.066319</td>\n",
       "      <td>0.433531</td>\n",
       "      <td>0.623669</td>\n",
       "      <td>1.678527</td>\n",
       "      <td>-2.044615</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>1.307580</td>\n",
       "      <td>-0.059874</td>\n",
       "      <td>-0.276768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>-1.050474</td>\n",
       "      <td>-0.492467</td>\n",
       "      <td>-3.057118</td>\n",
       "      <td>-2.102062</td>\n",
       "      <td>0.290074</td>\n",
       "      <td>-1.837421</td>\n",
       "      <td>0.771637</td>\n",
       "      <td>0.883183</td>\n",
       "      <td>0.827531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586739</td>\n",
       "      <td>-0.414671</td>\n",
       "      <td>-0.480104</td>\n",
       "      <td>-1.629912</td>\n",
       "      <td>3.934243</td>\n",
       "      <td>2.386098</td>\n",
       "      <td>-1.041708</td>\n",
       "      <td>0.388762</td>\n",
       "      <td>0.855838</td>\n",
       "      <td>-1.768747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>0.277096</td>\n",
       "      <td>-0.177303</td>\n",
       "      <td>1.520242</td>\n",
       "      <td>1.147505</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.343843</td>\n",
       "      <td>1.057434</td>\n",
       "      <td>1.925118</td>\n",
       "      <td>0.395960</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060054</td>\n",
       "      <td>-0.819456</td>\n",
       "      <td>-0.657092</td>\n",
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       "      <td>-1.769988</td>\n",
       "      <td>-2.901545</td>\n",
       "      <td>0.582610</td>\n",
       "      <td>3.461969</td>\n",
       "      <td>-1.540928</td>\n",
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       "<p>5 rows × 101 columns</p>\n",
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      "text/plain": [
       "         Date         0         1         2         3         4         5  \\\n",
       "0  2015-11-01 -0.773164  0.409429 -1.967424 -0.194265  1.589418 -0.806473   \n",
       "1  2015-11-08 -0.375792  1.282191 -1.769311 -1.768404  0.960811 -1.401841   \n",
       "2  2015-11-15  0.270060  1.896669  0.160804  0.100467 -1.593640 -0.673158   \n",
       "3  2015-11-22 -1.050474 -0.492467 -3.057118 -2.102062  0.290074 -1.837421   \n",
       "4  2015-11-29  0.277096 -0.177303  1.520242  1.147505  0.999619  0.343843   \n",
       "\n",
       "          6         7         8  ...        90        91        92        93  \\\n",
       "0  3.013131 -0.131107 -1.274766  ...  1.858297 -1.143137 -0.012872  2.545450   \n",
       "1 -0.138934  0.260522 -1.184208  ...  1.427782 -2.927658  0.504012 -0.062580   \n",
       "2  0.158351  1.709716 -0.976310  ...  1.151502 -1.066319  0.433531  0.623669   \n",
       "3  0.771637  0.883183  0.827531  ...  1.586739 -0.414671 -0.480104 -1.629912   \n",
       "4  1.057434  1.925118  0.395960  ...  1.060054 -0.819456 -0.657092  0.556108   \n",
       "\n",
       "         94        95        96        97        98        99  \n",
       "0 -1.408913  2.835881 -4.650385  2.829885 -3.892387 -2.027068  \n",
       "1  3.175248 -1.147623 -2.653756  2.155063 -1.433320 -1.319588  \n",
       "2  1.678527 -2.044615  0.735826  1.307580 -0.059874 -0.276768  \n",
       "3  3.934243  2.386098 -1.041708  0.388762  0.855838 -1.768747  \n",
       "4  3.519002 -1.769988 -2.901545  0.582610  3.461969 -1.540928  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Path to csv file:\n",
    "file_path = 'Embeddings/embeddings_medellin_100features.csv'\n",
    "\n",
    "# Read csv file\n",
    "file = pd.read_csv(file_path)\n",
    "\n",
    "# Show the dataframe before apply the function:\n",
    "# Number of rows to show:\n",
    "rows = 5 \n",
    "# show:\n",
    "file.head(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "578ec5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>201545</td>\n",
       "      <td>-0.375792</td>\n",
       "      <td>1.282191</td>\n",
       "      <td>-1.769311</td>\n",
       "      <td>-1.768404</td>\n",
       "      <td>0.960811</td>\n",
       "      <td>-1.401841</td>\n",
       "      <td>-0.138934</td>\n",
       "      <td>0.260522</td>\n",
       "      <td>-1.184208</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427782</td>\n",
       "      <td>-2.927658</td>\n",
       "      <td>0.504012</td>\n",
       "      <td>-0.062580</td>\n",
       "      <td>3.175248</td>\n",
       "      <td>-1.147623</td>\n",
       "      <td>-2.653756</td>\n",
       "      <td>2.155063</td>\n",
       "      <td>-1.433320</td>\n",
       "      <td>-1.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201546</td>\n",
       "      <td>0.270060</td>\n",
       "      <td>1.896669</td>\n",
       "      <td>0.160804</td>\n",
       "      <td>0.100467</td>\n",
       "      <td>-1.593640</td>\n",
       "      <td>-0.673158</td>\n",
       "      <td>0.158351</td>\n",
       "      <td>1.709716</td>\n",
       "      <td>-0.976310</td>\n",
       "      <td>...</td>\n",
       "      <td>1.151502</td>\n",
       "      <td>-1.066319</td>\n",
       "      <td>0.433531</td>\n",
       "      <td>0.623669</td>\n",
       "      <td>1.678527</td>\n",
       "      <td>-2.044615</td>\n",
       "      <td>0.735826</td>\n",
       "      <td>1.307580</td>\n",
       "      <td>-0.059874</td>\n",
       "      <td>-0.276768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201547</td>\n",
       "      <td>-1.050474</td>\n",
       "      <td>-0.492467</td>\n",
       "      <td>-3.057118</td>\n",
       "      <td>-2.102062</td>\n",
       "      <td>0.290074</td>\n",
       "      <td>-1.837421</td>\n",
       "      <td>0.771637</td>\n",
       "      <td>0.883183</td>\n",
       "      <td>0.827531</td>\n",
       "      <td>...</td>\n",
       "      <td>1.586739</td>\n",
       "      <td>-0.414671</td>\n",
       "      <td>-0.480104</td>\n",
       "      <td>-1.629912</td>\n",
       "      <td>3.934243</td>\n",
       "      <td>2.386098</td>\n",
       "      <td>-1.041708</td>\n",
       "      <td>0.388762</td>\n",
       "      <td>0.855838</td>\n",
       "      <td>-1.768747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201548</td>\n",
       "      <td>0.277096</td>\n",
       "      <td>-0.177303</td>\n",
       "      <td>1.520242</td>\n",
       "      <td>1.147505</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.343843</td>\n",
       "      <td>1.057434</td>\n",
       "      <td>1.925118</td>\n",
       "      <td>0.395960</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060054</td>\n",
       "      <td>-0.819456</td>\n",
       "      <td>-0.657092</td>\n",
       "      <td>0.556108</td>\n",
       "      <td>3.519002</td>\n",
       "      <td>-1.769988</td>\n",
       "      <td>-2.901545</td>\n",
       "      <td>0.582610</td>\n",
       "      <td>3.461969</td>\n",
       "      <td>-1.540928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date         0         1         2         3         4         5  \\\n",
       "0  201544 -0.773164  0.409429 -1.967424 -0.194265  1.589418 -0.806473   \n",
       "1  201545 -0.375792  1.282191 -1.769311 -1.768404  0.960811 -1.401841   \n",
       "2  201546  0.270060  1.896669  0.160804  0.100467 -1.593640 -0.673158   \n",
       "3  201547 -1.050474 -0.492467 -3.057118 -2.102062  0.290074 -1.837421   \n",
       "4  201548  0.277096 -0.177303  1.520242  1.147505  0.999619  0.343843   \n",
       "\n",
       "          6         7         8  ...        90        91        92        93  \\\n",
       "0  3.013131 -0.131107 -1.274766  ...  1.858297 -1.143137 -0.012872  2.545450   \n",
       "1 -0.138934  0.260522 -1.184208  ...  1.427782 -2.927658  0.504012 -0.062580   \n",
       "2  0.158351  1.709716 -0.976310  ...  1.151502 -1.066319  0.433531  0.623669   \n",
       "3  0.771637  0.883183  0.827531  ...  1.586739 -0.414671 -0.480104 -1.629912   \n",
       "4  1.057434  1.925118  0.395960  ...  1.060054 -0.819456 -0.657092  0.556108   \n",
       "\n",
       "         94        95        96        97        98        99  \n",
       "0 -1.408913  2.835881 -4.650385  2.829885 -3.892387 -2.027068  \n",
       "1  3.175248 -1.147623 -2.653756  2.155063 -1.433320 -1.319588  \n",
       "2  1.678527 -2.044615  0.735826  1.307580 -0.059874 -0.276768  \n",
       "3  3.934243  2.386098 -1.041708  0.388762  0.855838 -1.768747  \n",
       "4  3.519002 -1.769988 -2.901545  0.582610  3.461969 -1.540928  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the date in column date to epiweek:\n",
    "file['Date'] = file['Date'].apply(getEpiweek)\n",
    "\n",
    "# Show the dataframe after:\n",
    "# Number of rows to show:\n",
    "rows = 5 \n",
    "# show:\n",
    "file.head(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c43c7943",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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