idling-mind/GooglePhotosStats_analyze_1.ipynb

## GooglePhotosStats_analyze_1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "photos = pd.read_hdf('google_photo_data.hdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total number of media items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The length of the dataframe will give us an idea of how many media items are there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53517"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(photos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different types of media items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There may be different media types in the library. `mimeType` column will give us an idea about that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image/jpeg    52641\n",
       "video/mp4       508\n",
       "image/gif       231\n",
       "image/png       118\n",
       "image/raw        19\n",
       "Name: mimeType, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photos.mimeType.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yearwise count of media items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will be interesting to know how many images I was taking every ear and if there is any trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d73edd850>"
      ]
     },
     "execution_count": 5,
     "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": [
    "photos.groupby(photos.creationTime.dt.year).id.count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I realized that I have some images which had wrong dates set. ie; 1970!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which days of the week do I take most of the images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My expectation is that I'll take more images on the weekend. Let's see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d70b5c910>"
      ]
     },
     "execution_count": 6,
     "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": [
    "photos.creationTime.dt.dayofweek.hist(bins=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course! 5 and 6 are Saturday and Sunday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time of the day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about time of the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d7065f040>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/IuKh9niL6sYy87k2/TwwtpqVWWUfiYhH2/DPyA9p9IuIceA84D4KnhOjGPp62Xsy8510Q107IuJPV7tCx4vsrlWuer3yrcAfAOcCzwE3rm51hici3gJ8DfhYZv60f1mVc2IUQ99HPTSZebD9PAR8g27oq7IXIuIsgPbz0CrXZ1Vk5guZ+evM/A3wLxQ5LyLiJLrA/2Jmfr0VlzsnRjH0fdQDEBGnRMRvHZ0GLgYee/2tRt5eYGub3grcuYp1WTVHQ675KwqcFxERwG3Ak5l5U9+icufESN6R2y5B+zQvP+rh+lWu0tBFxO/T9e6he9zGlyq1Q0R8GZike3zuC8C1wL8DdwC/CzwDXJGZI/0h5xztMEk3tJPAfuBv+sa1R1JEvAf4T2Af8JtW/HG6cf1a58Qohr4k6dhGcXhHkjQHQ1+SCjH0JakQQ1+SCjH0JakQQ1+SCjH0JamQ/wdoC2PStguNnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "photos.creationTime.dt.hour.hist(bins=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostly in the afternoon. Surprisingly, I had many images even during the wee hours of the day. But I realized that they were from a different time zones when I checked these images."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "photostats",
   "language": "python",
   "name": "photostats"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}