Created using Colaboratory

Author: Tanu-N-Prabhu

Google_Trends_API_for Python.ipynb

@@ -96,16 +96,16 @@
       "metadata": {
         "id": "RFBb6m_momYt",
         "colab_type": "code",
+        "outputId": "6a2a88fb-611c-4110-9b91-9b50e3f69010",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 225
-        },
-        "outputId": "6a2a88fb-611c-4110-9b91-9b50e3f69010"
+        }
       },
       "source": [
         "!pip install pytrends"
       ],
-      "execution_count": 26,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -224,19 +224,19 @@
       "metadata": {
         "id": "8MB0udCqpD7T",
         "colab_type": "code",
+        "outputId": "e8bdf609-14a6-45e3-b840-d6c0ef914dfa",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 386
-        },
-        "outputId": "e8bdf609-14a6-45e3-b840-d6c0ef914dfa"
+        }
       },
       "source": [
         "pytrend.build_payload(kw_list=['Taylor Swift'])\n",
         "# Interest by Region\n",
         "df = pytrend.interest_by_region()\n",
         "df.head(10)"
       ],
-      "execution_count": 28,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -361,16 +361,16 @@
       "metadata": {
         "id": "Jv5fd7PBpUEQ",
         "colab_type": "code",
+        "outputId": "6bf1a66f-368b-4048-b53b-9ae44b20f1a8",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 837
-        },
-        "outputId": "6bf1a66f-368b-4048-b53b-9ae44b20f1a8"
+        }
       },
       "source": [
         "df.reset_index().plot(x='geoName', y='Taylor Swift', figsize=(120, 10), kind ='bar')"
       ],
-      "execution_count": 29,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -430,18 +430,18 @@
       "metadata": {
         "id": "CZxlpg2Vphiv",
         "colab_type": "code",
+        "outputId": "53369134-c52f-481d-9f5a-e033d235c435",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 202
-        },
-        "outputId": "53369134-c52f-481d-9f5a-e033d235c435"
+        }
       },
       "source": [
         "# Get Google Hot Trends data\n",
         "df = pytrend.trending_searches(pn='united_states')\n",
         "df.head()"
       ],
-      "execution_count": 30,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -562,18 +562,18 @@
       "metadata": {
         "id": "RR8sdilxp0TP",
         "colab_type": "code",
+        "outputId": "8b8f1dce-a548-4ebf-e601-3a7b524db80a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 202
-        },
-        "outputId": "8b8f1dce-a548-4ebf-e601-3a7b524db80a"
+        }
       },
       "source": [
         "# Get Google Top Charts\n",
         "df = pytrend.top_charts(2019, hl='en-US', tz=300, geo='GLOBAL')\n",
         "df.head()"
       ],
-      "execution_count": 32,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -681,18 +681,18 @@
       "metadata": {
         "id": "phF_2PZxqETV",
         "colab_type": "code",
+        "outputId": "e1944072-ae5e-40c7-bb38-cd8e8cc0dc37",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 202
-        },
-        "outputId": "e1944072-ae5e-40c7-bb38-cd8e8cc0dc37"
+        }
       },
       "source": [
         "# Get Google Keyword Suggestionskeywords = pytrend.suggestions(keyword='Mercedes Benz')\n",
         "df = pd.DataFrame(keywords)\n",
         "df.drop(columns= 'mid')   # This column makes no sense"
       ],
-      "execution_count": 33,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -806,18 +806,18 @@
       "metadata": {
         "id": "t1h_k1o3qL1o",
         "colab_type": "code",
+        "outputId": "edb93ab5-f9c6-43d7-b332-99058f4299cb",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 901
-        },
-        "outputId": "edb93ab5-f9c6-43d7-b332-99058f4299cb"
+        }
       },
       "source": [
         "# Related Queries, returns a dictionary of dataframes\n",
         "related_queries = pytrend.related_queries()\n",
         "related_queries.values()"
       ],
-      "execution_count": 35,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "execute_result",