{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "FB_Ads.ipynb", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "name": "python2", "display_name": "Python 2" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "096QxlK4ycS0", "colab_type": "text" }, "source": [ "# 1. Run Initial Ad Insights Query for User" ] }, { "cell_type": "code", "metadata": { "id": "qkvX8JFUIX0E", "colab_type": "code", "colab": {} }, "source": [ "#Error Count 138+1+1\n", "#Call Count 891+142\n", "import time\n", "import json\n", "import urllib2\n", "url = \"https://graph.facebook.com/v4.0/act_512042582544198/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-K-kopNAykg7", "colab_type": "text" }, "source": [ "# 2. Save Response to a Python Dataframe" ] }, { "cell_type": "code", "metadata": { "id": "7Onx_NE4teuV", "colab_type": "code", "colab": {} }, "source": [ "import pandas as pd\n", "next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "GcyE7zlGrwMS", "colab_type": "code", "colab": {} }, "source": [ "df=pd.DataFrame.from_dict(values['data'])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "YgQAUV1EbuVI", "colab_type": "code", "outputId": "0a5bdd93-ad9f-449c-b6ce-7e5f1bccacfa", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "df.head()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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campaign_namecpmdate_startdate_stopdevice_platformimpression_deviceimpressionsobjectiveplatform_positionpublisher_platformreachspend
0Khalid - AU (Free Spirit)1.9542772019-07-152019-08-13mobile_appandroid_smartphone4068LINK_CLICKSinstagram_storiesinstagram39607.95
1Khalid - AU (Free Spirit)02019-07-152019-08-13mobile_appandroid_tablet32LINK_CLICKSinstagram_storiesinstagram160
2Khalid - AU (Free Spirit)2.3222752019-07-152019-08-13mobile_appipad211LINK_CLICKSinstagram_storiesinstagram1680.49
3Khalid - AU (Free Spirit)1.5073012019-07-152019-08-13mobile_appiphone31573LINK_CLICKSinstagram_storiesinstagram2957647.59
4Khalid - AU (Free Spirit)02019-07-152019-08-13mobile_appipod35LINK_CLICKSinstagram_storiesinstagram350
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" ], "text/plain": [ " campaign_name cpm ... reach spend\n", "0 Khalid - AU (Free Spirit) 1.954277 ... 3960 7.95\n", "1 Khalid - AU (Free Spirit) 0 ... 16 0\n", "2 Khalid - AU (Free Spirit) 2.322275 ... 168 0.49\n", "3 Khalid - AU (Free Spirit) 1.507301 ... 29576 47.59\n", "4 Khalid - AU (Free Spirit) 0 ... 35 0\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 4 } ] }, { "cell_type": "code", "metadata": { "id": "DpUS7fzXIfnX", "colab_type": "code", "colab": {} }, "source": [ "import time\n", "import json\n", "import urllib2\n", "import pandas as pd\n", "url = \"https://graph.facebook.com/v4.0/act_10156308372288586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df_interim=pd.DataFrame.from_dict(values['data'])\n", "df_interim['account']='Myles'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ocTAADZlInkd", "colab_type": "code", "colab": {} }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_512042582544198/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df=pd.DataFrame.from_dict(values['data'])\n", "df['account']='Jenn'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Yn6JJ_XgIyAC", "colab_type": "code", "outputId": "50233e19-f7fb-4b4e-d410-738b161824a8", "colab": { "base_uri": "https://localhost:8080/", "height": 511 } }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_10156162965163586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df=pd.DataFrame.from_dict(values['data'])\n", "df['account']='Sam'" ], "execution_count": 0, "outputs": [ { "output_type": "error", "ename": "KeyError", "evalue": "ignored", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\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 4\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'paging'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'next'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'after'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\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 6\u001b[0m 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\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2657\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2658\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2660\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2661\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'next'" ] } ] }, { "cell_type": "code", "metadata": { "id": "sE_GxapSJEGU", "colab_type": "code", "colab": {} }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_1376605895888910/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "#next=pd.DataFrame.from_dict(values['paging'])['next']['before'].encode('utf-8')\n", "df=pd.DataFrame.from_dict(values['data'])\n", "df['account']='Joe'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "FUfgxvWhJdFW", "colab_type": "code", "colab": {} }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_2123931584507408/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "#next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df=pd.DataFrame.from_dict(values['data'])\n", "df['account']='Joe 2'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "jKWTL9IzJ8_7", "colab_type": "code", "colab": {} }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_10156352789338586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df=pd.DataFrame.from_dict(values['data'])\n", "df['account']='Devin 3'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ptbHqWmaKYJK", "colab_type": "code", "colab": {} }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_10154205820958586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "#next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df=pd.DataFrame.from_dict(values['data'])\n", "df['account']='Devin 2'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "z1e_SE_8Kjuc", "colab_type": "code", "colab": {} }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_108170885940775/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "#next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df=pd.DataFrame.from_dict(values['data'])\n", "df['account']='Epic Retarget'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "UenedxWAJRcY", "colab_type": "code", "outputId": "0f88378f-1804-4101-9295-16425028c5fe", "colab": { "base_uri": "https://localhost:8080/", "height": 187 } }, "source": [ "df_interim.count()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "campaign_name 25\n", "cpm 25\n", "date_start 25\n", "date_stop 25\n", "impressions 25\n", "objective 25\n", "reach 25\n", "spend 25\n", "account 25\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 12 } ] }, { "cell_type": "code", "metadata": { "id": "w6J76czrv5Fb", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "jzamGUH6yzha", "colab_type": "text" }, "source": [ "# 3. Cycle through next tokens and append each response to the Dataframe" ] }, { "cell_type": "code", "metadata": { "id": "xmRbjW6uIjlj", "colab_type": "code", "colab": {} }, "source": [ "#Myles\n", "import time\n", "import json\n", "import urllib2\n", "import pandas as pd\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10156308372288586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df_interim=pd.DataFrame.from_dict(values['data'])\n", " df_interim['account']='Myles'\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Myles'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_512042582544198/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Jenn'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Jenn'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10156162965163586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Sam'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Sam'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_1376605895888910/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_2123931584507408/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe 2'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe 2'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10156352789338586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 3'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 3'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10154205820958586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 2'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 2'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_108170885940775/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Epic Retarget'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Epic Retarget'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Y-jNhGNrghwk", "colab_type": "code", "outputId": "caee4a19-e5a4-4c48-99e4-d07a5d9aa15f", "colab": { "base_uri": "https://localhost:8080/", "height": 187 } }, "source": [ "df_interim.count()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "campaign_name 1074\n", "cpm 1074\n", "date_start 1074\n", "date_stop 1074\n", "impressions 1074\n", "objective 1074\n", "reach 1074\n", "spend 1074\n", "account 1074\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 14 } ] }, { "cell_type": "code", "metadata": { "id": "3VYUvVu7IXFn", "colab_type": "code", "colab": {} }, "source": [ "import time\n", "import json\n", "import urllib2\n", "import pandas as pd\n", "for y in range(1):\n", " #time.sleep(3600)\n", " count=0\n", " #Myles\n", " for x in range(10000):\n", " time.sleep(20)\n", " url = \"https://graph.facebook.com/v4.0/act_10156308372288586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&breakdowns=device_platform%2Cpublisher_platform%2Cplatform_position%2Cimpression_device&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " #data = response.read()\n", " count+=1\n", " #Sam\n", " time.sleep(20)\n", " url = \"https://graph.facebook.com/v4.0/act_10156162965163586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&breakdowns=device_platform%2Cpublisher_platform%2Cplatform_position%2Cimpression_device&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " #data = response.read()\n", " count+=1\n", " #Jenn\n", " #time.sleep(23)\n", " #url = \"https://graph.facebook.com/v4.0/act_512042582544198/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&breakdowns=device_platform%2Cpublisher_platform%2Cplatform_position%2Cimpression_device&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " #response = urllib2.urlopen(url)\n", " #data = response.read()\n", " #count+=1\n", " #Devin 3\n", " #time.sleep(10)\n", " #url = \"https://graph.facebook.com/v4.0/act_10156352789338586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&breakdowns=device_platform%2Cpublisher_platform%2Cplatform_position%2Cimpression_device&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " #response = urllib2.urlopen(url)\n", " #data = response.read()\n", " #count+=1\n", " #time.sleep(3600)\n", " #values = json.loads(data)\n", " #next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " #df=pd.DataFrame.from_dict(values['data'])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "HdNoE4a1ZG15", "colab_type": "code", "outputId": "4a51c9b5-a12f-4d5e-dbab-7a84682796b3", "colab": { "base_uri": "https://localhost:8080/", "height": 169 } }, "source": [ "count" ], "execution_count": 0, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "ignored", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'count' is not defined" ] } ] }, { "cell_type": "code", "metadata": { "id": "4hzjqwbYyJ8h", "colab_type": "code", "outputId": "ffc37f2b-3944-4103-9ffe-4065d431a8e4", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "next" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'https://graph.facebook.com/v4.0/act_512042582544198/insights?access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&breakdowns=device_platform%2Cpublisher_platform%2Cplatform_position%2Cimpression_device&level=ad&limit=25&after=MzU0OQZDZD'" ] }, "metadata": { "tags": [] }, "execution_count": 6 } ] }, { "cell_type": "markdown", "metadata": { "id": "kck_drTyzBim", "colab_type": "text" }, "source": [ "# 4. Analyze Campaign Effectiveness" ] }, { "cell_type": "code", "metadata": { "id": "DjB1vpUAJavU", "colab_type": "code", "outputId": "bb8ab205-1fef-4553-f12e-bbb414f7645e", "colab": { "base_uri": "https://localhost:8080/", "height": 187 } }, "source": [ "df_interim.count()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "campaign_name 1074\n", "cpm 1074\n", "date_start 1074\n", "date_stop 1074\n", "impressions 1074\n", "objective 1074\n", "reach 1074\n", "spend 1074\n", "account 1074\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 26 } ] }, { "cell_type": "code", "metadata": { "id": "0HXPB10iI0w_", "colab_type": "code", "outputId": "9d4840b0-c933-470a-c0c9-6cd6ef526280", "colab": { "base_uri": "https://localhost:8080/", "height": 68 } }, "source": [ "from fastparquet import write\n", "write('ads.parq', df_interim)\n", "!gsutil cp ads.parq gs://epic-social/ads.parq" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Copying file://ads.parq [Content-Type=application/octet-stream]...\n", "-\n", "Operation completed over 1 objects/1.1 MiB. \n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "ffbSI0L27OgD", "colab_type": "code", "outputId": "bb5fb44b-8bf4-41a9-f6db-c83e0cdfc338", "colab": { "base_uri": "https://localhost:8080/", "height": 324 } }, "source": [ "df_interim.to_json('ads.json')\n", "!gsutil cp ads.json gs://epic-social/ads.json" ], "execution_count": 0, "outputs": [ { "output_type": "error", "ename": "ValueError", "evalue": "ignored", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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index)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0mdouble_precision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdouble_precision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_ascii\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_ascii\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[0mdate_unit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdate_unit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault_handler\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdefault_handler\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 index=index).write()\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/json/json.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, orient, date_format, double_precision, ensure_ascii, date_unit, index, default_handler)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_copy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format_axes\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 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_format_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/usr/local/lib/python2.7/dist-packages/pandas/io/json/json.pyc\u001b[0m in \u001b[0;36m_format_axes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 145\u001b[0m 'index', 'columns'):\n\u001b[1;32m 146\u001b[0m raise ValueError(\"DataFrame index must be unique for orient=\"\n\u001b[0;32m--> 147\u001b[0;31m \"'{orient}'.\".format(orient=self.orient))\n\u001b[0m\u001b[1;32m 148\u001b[0m if not self.obj.columns.is_unique and self.orient in (\n\u001b[1;32m 149\u001b[0m 'index', 'columns', 'records'):\n", "\u001b[0;31mValueError\u001b[0m: DataFrame index must be unique for orient='columns'." ] } ] }, { "cell_type": "code", "metadata": { "id": "JIIRNe1cKcSX", "colab_type": "code", "outputId": "ea189e9f-0eda-4918-ff4b-87daa0dd4664", "colab": { "base_uri": "https://localhost:8080/", "height": 119 } }, "source": [ "from google.colab import auth\n", "auth.authenticate_user()\n", "\n", "# https://cloud.google.com/resource-manager/docs/creating-managing-projects\n", "project_id = 'alien-iterator-227622'\n", "!gcloud config set project {project_id}" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Updated property [core/project].\n", "\n", "\n", "To take a quick anonymous survey, run:\n", " $ gcloud alpha survey\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "9PF9NULeamMT", "colab_type": "code", "outputId": "33b5a1f0-69bd-4de6-d5a4-f877b5f6f8af", "colab": { "base_uri": "https://localhost:8080/", "height": 479 } }, "source": [ "!pip install fastparquet" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Collecting fastparquet\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/58/49/dccb790fa17ab3fbf84a6b848050083c7a1899e9586000e34e3e4fbf5538/fastparquet-0.3.2.tar.gz (151kB)\n", "\u001b[K |████████████████████████████████| 153kB 8.8MB/s \n", "\u001b[?25hRequirement already satisfied: pandas>=0.19 in /usr/local/lib/python2.7/dist-packages (from fastparquet) (0.24.2)\n", "Requirement already satisfied: numba>=0.28 in /usr/local/lib/python2.7/dist-packages (from fastparquet) (0.40.1)\n", "Requirement already satisfied: 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/root/.cache/pip/wheels/be/36/81/0f93ba89a1cb7887c91937948519840a72c0ffdd57cac0ae8f\n", "Successfully built fastparquet thrift\n", "Installing collected packages: thrift, fastparquet\n", "Successfully installed fastparquet-0.3.2 thrift-0.11.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "VO0qFXZe5dZS", "colab_type": "code", "outputId": "063f9cb8-3ff9-46c9-a368-52e4fbd9016e", "colab": { "base_uri": "https://localhost:8080/", "height": 102 } }, "source": [ "!pip install pyarrow" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: pyarrow in /usr/local/lib/python2.7/dist-packages (0.13.0)\n", "Requirement already satisfied: six>=1.0.0 in /usr/local/lib/python2.7/dist-packages (from pyarrow) (1.12.0)\n", "Requirement already satisfied: enum34>=1.1.6; python_version < \"3.4\" in /usr/local/lib/python2.7/dist-packages (from pyarrow) (1.1.6)\n", "Requirement already satisfied: futures; python_version < \"3.2\" 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"name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "s_3Y0Ox5KpnL", "colab_type": "code", "outputId": "31c7eb39-cdbb-4f0c-8e9c-9507f2e21b93", "colab": { "base_uri": "https://localhost:8080/", "height": 394 } }, "source": [ "df2.head()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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accountdatedescriptionexpandedLinksidlinkmediamessageplatformplatformIdpostUrlscorestatisticssubscriberCounttypeupdated
0{u'subscriberCount': 46527589, u'profileImage'...2019-06-25 23:52:14Scream therapy @camila_cabelloNaN63027061462NaN[{u'url': u'https://scontent.cdninstagram.com/...NaNInstagram2074390522188857733_212742998https://www.instagram.com/p/BzJtzB4gQGF/14482525.0{u'expected': {u'commentCount': 18209, u'viewC...45463841.0video2019-07-04 19:10:33
1{u'subscriberCount': 46527589, u'profileImage'...2019-06-30 05:22:23Thank you for all the love on Señorita ♥️ @cam...NaN63449743873NaN[{u'url': u'https://scontent.cdninstagram.com/...NaNInstagram2077455948334515185_212742998https://www.instagram.com/p/BzUmy2dAEPx/7011901.0{u'expected': {u'commentCount': 17756, u'viewC...46129592.0video2019-07-04 19:10:33
2{u'subscriberCount': 46527589, u'profileImage'...2019-06-21 17:39:48Señorita...@camila_cabello 🖤🌹NaN62681869480NaN[{u'url': u'https://scontent.xx.fbcdn.net/v/t5...NaNInstagram2071304744961541723_212742998https://www.instagram.com/p/By-wLD5A4Zb/6992520.0{u'expected': {u'commentCount': 29663, u'favor...45007944.0album2019-07-04 19:10:33
3{u'subscriberCount': 3899124, u'profileImage':...2019-06-27 20:10:46How to create Havana by Camila Cabello 😂 @imal...NaN63242103054NaN[{u'url': u'https://scontent.xx.fbcdn.net/v/t5...NaNInstagram2075728995598371345_4686316577https://www.instagram.com/p/BzOeIYYnooR/6934390.0{u'expected': {u'commentCount': 3219, u'viewCo...3789428.0video2019-07-04 19:16:59
4{u'subscriberCount': 46527589, u'profileImage'...2019-06-20 18:01:40#Señorita video with @camila_cabello midnight ...NaN62578870210NaN[{u'url': u'https://scontent.xx.fbcdn.net/v/t5...NaNInstagram2070590974857563499_212742998https://www.instagram.com/p/By8N4Vrg81r/4070097.0{u'expected': {u'commentCount': 26915, u'favor...44973020.0photo2019-07-04 19:10:33
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" ], "text/plain": [ " account ... updated\n", "0 {u'subscriberCount': 46527589, u'profileImage'... ... 2019-07-04 19:10:33\n", "1 {u'subscriberCount': 46527589, u'profileImage'... ... 2019-07-04 19:10:33\n", "2 {u'subscriberCount': 46527589, u'profileImage'... ... 2019-07-04 19:10:33\n", "3 {u'subscriberCount': 3899124, u'profileImage':... ... 2019-07-04 19:16:59\n", "4 {u'subscriberCount': 46527589, u'profileImage'... ... 2019-07-04 19:10:33\n", "\n", "[5 rows x 16 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 39 } ] }, { "cell_type": "code", "metadata": { "id": "QvaHA5hzATmQ", "colab_type": "code", "colab": {} }, "source": [ "import json\n", "import urllib2\n", "url=''\n", "response=''\n", "data=''\n", "values=''\n", "df=''\n", "df2=''\n", "page=''\n", "url = \"https://api.crowdtangle.com/lists?token=07PCsFU8FOkUKLxmThNSHfLK7UP7KUWnHK0E7kCz\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "7z4GyRq8BEog", "colab_type": "code", "outputId": "2818b03c-bb26-433e-ee9b-77f964a7ba40", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "values" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{u'result': {u'lists': [{u'id': 1124537,\n", " u'title': u'Camila Cabello',\n", " u'type': u'LIST'},\n", " {u'id': 1124559, u'title': u'French Montana', u'type': u'LIST'},\n", " {u'id': 1124561, u'title': u'AJ Mitchell', u'type': u'LIST'},\n", " {u'id': 1124562, u'title': u'Zara Larsson', u'type': u'LIST'},\n", " {u'id': 1124563, u'title': u'Tyla Yaweh', u'type': u'LIST'},\n", " {u'id': 1157446, u'title': u'Meghan Trainor', u'type': u'LIST'},\n", " {u'id': 1161953, u'title': u'DJ Khaled', u'type': u'LIST'},\n", " {u'id': 1183368, u'title': u'Senorita', u'type': u'LIST'}]},\n", " u'status': 200}" ] }, "metadata": { "tags": [] }, "execution_count": 3 } ] }, { "cell_type": "code", "metadata": { "id": "ovHcp46RBG1x", "colab_type": "code", "outputId": "bd7d8895-677e-42b1-ed56-d165b39653dd", "colab": { "base_uri": "https://localhost:8080/", "height": 156 } }, "source": [ "!curl -G \\\n", "!-d \"level=ad\" \\\n", "!-d \"fields=impressions,ad_id\" \\\n", "!-d \"access_token=EAAGTXZCl1AJUBAC3xy1WMOBTEyP3vbVGxalpoZCZAoVyJhJb3qPsWF7DEOljrMdfirZA7JOQPwp2IvbwGGMrjVjl6zgXnZB2ZCFS1ZBWqRFM0ZAJ2WlqSzyaIlbVdNwylgcWBbPi64yK9DrZBjHWZCDMZBENNrtoH7HYne8IEXDPcndBOU73ENMt6tQ4CeiKKb50jsZD\" \\\n", "!\"https://graph.facebook.com/v4.0/act_512042582544198/insights\"" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "curl: (6) Could not resolve host: !-d\n", "curl: (6) Could not resolve host: level=ad\n", "curl: (6) Could not resolve host: !-d\n", "curl: (6) Could not resolve host: fields=impressions,ad_id\n", "curl: (6) Could not resolve host: !-d\n", "curl: (6) Could not resolve host: access_token=EAAGTXZCl1AJUBAC3xy1WMOBTEyP3vbVGxalpoZCZAoVyJhJb3qPsWF7DEOljrMdfirZA7JOQPwp2IvbwGGMrjVjl6zgXnZB2ZCFS1ZBWqRFM0ZAJ2WlqSzyaIlbVdNwylgcWBbPi64yK9DrZBjHWZCDMZBENNrtoH7HYne8IEXDPcndBOU73ENMt6tQ4CeiKKb50jsZD\n", "curl: (1) Protocol \"!https\" not supported or disabled in libcurl\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "gonS4Xc_P-lI", "colab_type": "code", "outputId": "1f0cfb14-c683-4ffe-e963-38845568c31e", "colab": { "base_uri": "https://localhost:8080/", "height": 477 } }, "source": [ "url = \"https://graph.facebook.com/v4.0/act_10156308372288586/insights?level=ad&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", "response = urllib2.urlopen(url)\n", "data = response.read()\n", "values = json.loads(data)\n", "next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "df_interim=pd.DataFrame.from_dict(values['data'])\n", "df_interim['account']='Myles'\n", "for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Myles'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')" ], "execution_count": 0, "outputs": [ { "output_type": "error", "ename": "KeyError", "evalue": "ignored", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m 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"8a8345a4-e516-4157-c807-849ce03cda29", "colab": { "base_uri": "https://localhost:8080/", "height": 187 } }, "source": [ "df_interim.count()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "campaign_name 303\n", "cpm 303\n", "date_start 303\n", "date_stop 303\n", "impressions 303\n", "objective 303\n", "reach 303\n", "spend 303\n", "account 303\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 12 } ] }, { "cell_type": "code", "metadata": { "id": "QGYcn2yGqqrM", "colab_type": "code", "outputId": "e0f6042d-8b4c-48c2-cb7d-09aef99cebc0", "colab": { "base_uri": "https://localhost:8080/", "height": 111 } }, "source": [ "pd.DataFrame.from_dict(values['paging'])" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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afterMjQZDhttps://graph.facebook.com/v4.0/act_5120425825...
beforeMAZDZDhttps://graph.facebook.com/v4.0/act_5120425825...
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" ], "text/plain": [ " cursors next\n", "after MjQZD https://graph.facebook.com/v4.0/act_5120425825...\n", "before MAZDZD https://graph.facebook.com/v4.0/act_5120425825..." ] }, "metadata": { "tags": [] }, "execution_count": 51 } ] }, { "cell_type": "code", "metadata": { "id": "tI8HUjbfQmG7", "colab_type": "code", "colab": {} }, "source": [ "https://graph.facebook.com/v4.0/act_512042582544198/insights?access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&breakdowns=device_platform%2Cpublisher_platform%2Cplatform_position%2Cimpression_device&level=ad&limit=25&after=MjQZD'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "oOMt0fhSQwBS", "colab_type": "code", "colab": {} }, "source": [ "https://graph.facebook.com/v4.0/act_512042582544198/insights?access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cobjective&breakdowns=device_platform%2Cpublisher_platform%2Cplatform_position%2Cimpression_device&level=ad&limit=25&after=MjQZD" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "JHKi6ZdpD886", "colab_type": "code", "colab": {} }, "source": [ "#Myles\n", "import time\n", "import json\n", "import urllib2\n", "import pandas as pd\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10156308372288586/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df_interim=pd.DataFrame.from_dict(values['data'])\n", " df_interim['account']='Myles'\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Myles'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_512042582544198/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Jenn'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Jenn'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10156162965163586/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Sam'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Sam'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_1376605895888910/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_2123931584507408/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe 2'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Joe 2'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10156352789338586/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 3'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 3'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_10154205820958586/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 2'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Devin 2'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass\n", "\n", "try:\n", " url = \"https://graph.facebook.com/v4.0/act_108170885940775/insights?level=campaign&time_range={'since':'2010-01-01','until':'2019-09-27'}&fields=campaign_name%2Cspend%2Creach%2Cimpressions%2Ccpm%2Cclicks%2Ccpc%2Cobjective&access_token=EAAGTXZCl1AJUBABQZBXNtjUJt160w7s0YltBMTSEvpuowRSsp90MqgnM7jHHT4qYMIESqEUgFYpxXwtQN5vMzQdZAFRKTAStPVP1SMGspIIZCeUAZBHmH900HwLGdd5JZBDxdk7ZC0DdkdxH3aoCKu9ExaMltMz9djOXe8YYE4W0gZDZD\"\n", " response = urllib2.urlopen(url)\n", " data = response.read()\n", " values = json.loads(data)\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Epic Retarget'\n", " df_interim=pd.concat([df_interim,df])\n", " for x in range(10000):\n", " response = urllib2.urlopen(next)\n", " data = response.read()\n", " values = json.loads(data)\n", " df=pd.DataFrame.from_dict(values['data'])\n", " df['account']='Epic Retarget'\n", " df_interim=pd.concat([df_interim,df])\n", " next=pd.DataFrame.from_dict(values['paging'])['next']['after'].encode('utf-8')\n", "except:\n", " pass" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "8exo3lzND9n3", "colab_type": "code", "outputId": "37ac24d2-a813-4c91-81cf-4fa8ccccbcce", "colab": { "base_uri": "https://localhost:8080/", "height": 221 } }, "source": [ "df_interim.count()" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "campaign_name 7023\n", "clicks 7023\n", "cpc 7006\n", "cpm 7023\n", "date_start 7023\n", "date_stop 7023\n", "impressions 7023\n", "objective 7023\n", "reach 7023\n", "spend 7023\n", "account 7023\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 22 } ] }, { "cell_type": "code", "metadata": { "id": "SlBSvMhFHgtU", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] } ] }