|
4 | 4 | "cell_type": "code",
|
5 | 5 | "execution_count": 1,
|
6 | 6 | "metadata": {
|
7 |
| - "collapsed": false, |
8 | 7 | "scrolled": true
|
9 | 8 | },
|
10 | 9 | "outputs": [
|
|
39 | 38 | "cell_type": "code",
|
40 | 39 | "execution_count": 2,
|
41 | 40 | "metadata": {
|
42 |
| - "collapsed": false, |
43 | 41 | "scrolled": true
|
44 | 42 | },
|
45 | 43 | "outputs": [
|
|
126 | 124 | {
|
127 | 125 | "cell_type": "code",
|
128 | 126 | "execution_count": 163,
|
129 |
| - "metadata": { |
130 |
| - "collapsed": false |
131 |
| - }, |
| 127 | + "metadata": {}, |
132 | 128 | "outputs": [
|
133 | 129 | {
|
134 | 130 | "name": "stdout",
|
|
172 | 168 | {
|
173 | 169 | "cell_type": "code",
|
174 | 170 | "execution_count": 164,
|
175 |
| - "metadata": { |
176 |
| - "collapsed": false |
177 |
| - }, |
| 171 | + "metadata": {}, |
178 | 172 | "outputs": [
|
179 | 173 | {
|
180 | 174 | "name": "stdout",
|
|
196 | 190 | {
|
197 | 191 | "cell_type": "code",
|
198 | 192 | "execution_count": 165,
|
199 |
| - "metadata": { |
200 |
| - "collapsed": false |
201 |
| - }, |
| 193 | + "metadata": {}, |
202 | 194 | "outputs": [
|
203 | 195 | {
|
204 | 196 | "name": "stdout",
|
|
215 | 207 | "print(\"Validating a single record:\", flush=True)\n",
|
216 | 208 | "print(gtrends_json['data'][0], flush=True)\n",
|
217 | 209 | "\n",
|
218 |
| - "# Convert to pandas df and handle string NaN's coming through data engine\n", |
| 210 | + "# Convert to pandas df\n", |
219 | 211 | "gtrends = pd.DataFrame.from_records(gtrends_json['data'])\\\n",
|
220 |
| - " .pivot(index='date', values='interest', columns='keyword')\\\n", |
221 |
| - " .replace({'NaN': None})\\\n", |
222 |
| - " .fillna(method='pad')\n", |
| 212 | + " .pivot(index='date', values='interest', columns='keyword')\n", |
| 213 | + "\n", |
| 214 | + "# Deal with potential nans\n", |
| 215 | + "for col in gtrends.columns:\n", |
| 216 | + " if 'NaN' in gtrends[col].values:\n", |
| 217 | + " gtrends[col].replace({'NaN': None}, inplace=True)\n", |
| 218 | + " else:\n", |
| 219 | + " continue\n", |
| 220 | + "\n", |
| 221 | + "# If there are nans, fill using pad method\n", |
| 222 | + "gtrends.fillna(method='pad', inplace=True)\n", |
223 | 223 | "\n",
|
224 | 224 | "# Set proper date format\n",
|
225 | 225 | "gtrends.index = pd.to_datetime(gtrends.index)\n",
|
|
231 | 231 | {
|
232 | 232 | "cell_type": "code",
|
233 | 233 | "execution_count": 108,
|
234 |
| - "metadata": { |
235 |
| - "collapsed": false |
236 |
| - }, |
| 234 | + "metadata": {}, |
237 | 235 | "outputs": [
|
238 | 236 | {
|
239 | 237 | "name": "stdout",
|
|
339 | 337 | {
|
340 | 338 | "cell_type": "code",
|
341 | 339 | "execution_count": 140,
|
342 |
| - "metadata": { |
343 |
| - "collapsed": false |
344 |
| - }, |
| 340 | + "metadata": {}, |
345 | 341 | "outputs": [
|
346 | 342 | {
|
347 | 343 | "name": "stdout",
|
|
501 | 497 | {
|
502 | 498 | "cell_type": "code",
|
503 | 499 | "execution_count": 143,
|
504 |
| - "metadata": { |
505 |
| - "collapsed": false |
506 |
| - }, |
| 500 | + "metadata": {}, |
507 | 501 | "outputs": [
|
508 | 502 | {
|
509 | 503 | "name": "stdout",
|
|
562 | 556 | {
|
563 | 557 | "cell_type": "code",
|
564 | 558 | "execution_count": 162,
|
565 |
| - "metadata": { |
566 |
| - "collapsed": false |
567 |
| - }, |
| 559 | + "metadata": {}, |
568 | 560 | "outputs": [
|
569 | 561 | {
|
570 | 562 | "name": "stdout",
|
|
0 commit comments