web traffic.ipynb

{
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r'C:\\Users\\joseph\\Downloads\\query_analytics.xlsx', engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df\n",
    "df2 = df[df[\"Search Query\"].str.contains('cyt') == False]\n",
    "df2 = df2[df2.Clicks > 0]\n",
    "#df2 = df2[df2[\"Search Query\"].str.contains('c.* y.* t.*') == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cyts = df[df[\"Search Query\"].str.contains('.*c.* y.* t.*', na = False)]\n",
    "print(df_cyts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_best = df2[df2['CTR']>df2.CTR.quantile(.5)] # above average CTR\n",
    "df_best = df2[df2['Impressions']>df2.Impressions.quantile(.5)] # above average impressions\n",
    "df_best = df_best[df_best['CTR']>df_best['CTR'].quantile(.5)] # below average searchrank\n",
    "df_best = df_best.sort_values(by='CTR', ascending=False)\n",
    "df_best['CTR'] = df_best['CTR']/100 # make these Excel-readable percentages\n",
    "\n",
    "df_best.describe()"
   ]
  }
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