{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "last_fm_similar_artists.ipynb",
"provenance": [],
"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](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "code",
"metadata": {
"id": "8sQq-fGL2D9h",
"colab_type": "code",
"outputId": "ae6c27c7-a45d-400b-e8cf-df35e82abe4c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 130
}
},
"source": [
"http://ws.audioscrobbler.com/2.0/?method=artist.getsimilar&artist=shakira&api_key=b44f3df9ff5702f634cad9ad4f401cfb&format=json"
],
"execution_count": 0,
"outputs": [
{
"output_type": "error",
"ename": "SyntaxError",
"evalue": "ignored",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m http://ws.audioscrobbler.com/2.0/?method=artist.getsimilar&artist=shakira&api_key=b44f3df9ff5702f634cad9ad4f401cfb&format=json\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PjE1uNNj3yE-",
"colab_type": "code",
"outputId": "2685cf08-fdf1-4791-89e2-529c30d4346c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"!pip install urllib"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[31mERROR: Could not find a version that satisfies the requirement urllib (from versions: none)\u001b[0m\n",
"\u001b[31mERROR: No matching distribution found for urllib\u001b[0m\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lsHPdmmA3n8M",
"colab_type": "code",
"colab": {}
},
"source": [
"import json\n",
"import urllib2\n",
"url = \"http://ws.audioscrobbler.com/2.0/?method=artist.getsimilar&artist=camila+cabello&api_key=b44f3df9ff5702f634cad9ad4f401cfb&format=json\"\n",
"response = urllib2.urlopen(url)\n",
"data = response.read()\n",
"values = json.loads(data)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TvHU_-m84OjN",
"colab_type": "code",
"colab": {}
},
"source": [
"l=[]\n",
"for i,x in enumerate(values['similarartists']['artist']):\n",
" l.append(values['similarartists']['artist'][i]['name'].encode('utf-8'))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SU4m1aMx8VQN",
"colab_type": "code",
"outputId": "46a171cd-790e-4c5a-9c9b-b4aad9a4d786",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"values['similarartists']['@attr']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{u'artist': u'Camila Cabello'}"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hQ0raJTY6ooh",
"colab_type": "code",
"outputId": "93e7f538-29c2-4973-aa61-f1deb3ef2beb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"l"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Fifth Harmony',\n",
" 'Lauren Jauregui',\n",
" 'Selena Gomez',\n",
" 'Ariana Grande',\n",
" 'Shawn Mendes',\n",
" 'Dinah Jane',\n",
" 'Sabrina Carpenter',\n",
" 'Taylor Swift',\n",
" 'Madison Beer',\n",
" 'Little Mix',\n",
" 'Demi Lovato',\n",
" 'Machine Gun Kelly & Camila Cabello',\n",
" 'Bebe Rexha',\n",
" 'Dua Lipa',\n",
" 'Halsey',\n",
" 'Zara Larsson',\n",
" 'Melanie Martinez',\n",
" 'Alessia Cara',\n",
" 'Liam Payne',\n",
" 'Selena Gomez & the Scene',\n",
" 'Katy Perry',\n",
" 'Julia Michaels',\n",
" 'Hailee Steinfeld',\n",
" 'J\\xc3\\xa3o',\n",
" 'Justin Bieber',\n",
" 'Ava Max',\n",
" 'Lu\\xc3\\xadsa Sonza',\n",
" 'Bea Miller',\n",
" 'Anitta',\n",
" 'Louis Tomlinson',\n",
" 'Becky G',\n",
" 'Miley Cyrus',\n",
" 'Zayn',\n",
" 'Selena Gomez & Marshmello',\n",
" 'Sofia Carson',\n",
" 'Iggy Azalea',\n",
" 'Ashley O',\n",
" 'Mabel',\n",
" 'Rihanna',\n",
" 'Rita Ora',\n",
" 'Niall Horan',\n",
" 'Troye Sivan',\n",
" 'Ariana Grande & Victoria Mon\\xc3\\xa9t',\n",
" 'Meghan Trainor',\n",
" 'IZA',\n",
" 'Anne-Marie',\n",
" 'Manu Gavassi',\n",
" 'Charlotte Lawrence',\n",
" 'Charlie Puth',\n",
" 'Bridgit Mendler',\n",
" 'Tove Lo',\n",
" 'Jessie J, Ariana Grande & Nicki Minaj',\n",
" 'Hayley Kiyoko',\n",
" \"olivia o'brien\",\n",
" 'Anah\\xc3\\xad',\n",
" 'Lexa',\n",
" 'Ruel',\n",
" 'Billie Eilish',\n",
" 'Cardi B, Bad Bunny & J Balvin',\n",
" 'PRETTYMUCH',\n",
" 'Maggie Lindemann',\n",
" 'Ke$ha',\n",
" 'Ludmilla',\n",
" 'Pia Mia',\n",
" 'Day',\n",
" 'Ashley Tisdale',\n",
" 'Dove Cameron',\n",
" 'Lea Michele',\n",
" 'Kim Petras',\n",
" 'Nick Jonas',\n",
" 'Cher Lloyd',\n",
" 'Lorde',\n",
" 'Dua Lipa & BLACKPINK',\n",
" 'Zendaya',\n",
" 'benny blanco, Tainy, Selena Gomez & J Balvin',\n",
" 'Kygo & Selena Gomez',\n",
" 'Britney Spears',\n",
" 'Jessie J',\n",
" 'Melim',\n",
" 'Bebe Rexha & Florida Georgia Line',\n",
" 'Lu\\xc3\\xadsa Sonza & Pabllo Vittar',\n",
" 'Mark Ronson',\n",
" \"why don't we\",\n",
" 'Topic & Ally Brooke',\n",
" 'Sam Smith',\n",
" 'Lady Gaga',\n",
" 'Fletcher',\n",
" 'Gloria Groove',\n",
" 'Carly Rae Jepsen',\n",
" 'Nicki Minaj',\n",
" 'Dulce Mar\\xc3\\xada',\n",
" 'Liam Payne & Rita Ora',\n",
" 'Avril Lavigne',\n",
" 'Miley Cyrus, Swae Lee & Mike WiLL Made-It',\n",
" 'The Vamps',\n",
" 'Austin Mahone',\n",
" 'The Pussycat Dolls',\n",
" 'China Anne McClain',\n",
" 'Beyonc\\xc3\\xa9',\n",
" 'Fergie']"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TBe7rFrK3vJI",
"colab_type": "code",
"outputId": "bf24f532-799d-403e-d57e-f2401e78c92d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 324
}
},
"source": [
"import pandas as pd\n",
"df=pd.DataFrame.from_dict(values['similarartists'])"
],
"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[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\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'similarartists'\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[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mfrom_dict\u001b[0;34m(cls, data, orient, dtype, columns)\u001b[0m\n\u001b[1;32m 1136\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'only recognize index or columns for orient'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1138\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\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 1139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1140\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\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/core/frame.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 390\u001b[0m dtype=dtype, copy=copy)\n\u001b[1;32m 391\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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--> 392\u001b[0;31m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\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 393\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\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 394\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\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/internals/construction.pyc\u001b[0m in \u001b[0;36minit_dict\u001b[0;34m(data, index, columns, dtype)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\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 213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals/construction.pyc\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\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 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\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/internals/construction.pyc\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m raise ValueError('Mixing dicts with non-Series may lead to '\n\u001b[0m\u001b[1;32m 321\u001b[0m 'ambiguous ordering.')\n\u001b[1;32m 322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Mixing dicts with non-Series may lead to ambiguous ordering."
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NDGwDLP54LSf",
"colab_type": "code",
"outputId": "36a065f5-c87f-47ef-90ae-8ccf51f5ea75",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 307
}
},
"source": [
"df=pd.read_json(values)"
],
"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 \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/json/json.pyc\u001b[0m in \u001b[0;36mread_json\u001b[0;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 412\u001b[0m filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(\n\u001b[0;32m--> 413\u001b[0;31m \u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompression\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 414\u001b[0m )\n\u001b[1;32m 415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/common.pyc\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding, compression, mode)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\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 231\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Invalid file path or buffer object type: {_type}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\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 233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Invalid file path or buffer object type: "
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VqoFYgqB5pfb",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}