mirror of
https://github.com/AllenDowney/AstronomicalData.git
synced 2025-12-12 15:50:09 -08:00
Updating solutions
This commit is contained in:
Allen Downey
@@ -1341,7 +1341,7 @@
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}
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],
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"source": [
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"## Solution\n",
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"# Solution\n",
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"\n",
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"radius = 5 * u.arcmin\n",
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"print(radius)\n",
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@@ -14,13 +14,11 @@
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"\n",
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"objectives:\n",
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"\n",
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"- \"Upload a table to the Gaia server.\"\n",
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"\n",
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"- \"Write ADQL queries involving `JOIN` operations.\"\n",
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"\n",
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"keypoints:\n",
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"\n",
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"- \"Use `JOIN` operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.\"\n",
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"- \"Use `JOIN` operations to combine data from multiple tables in a database, using some kind of identifier to match up records from one table with records from another.\"\n",
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"\n",
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"- \"This is another example of a practice we saw in the previous notebook, moving the computation to the data.\"\n",
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"\n",
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@@ -1097,34 +1095,7 @@
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"WHERE 1=CONTAINS(\n",
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" POINT(gaia.ra, gaia.dec),\n",
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" CIRCLE(88.8, 7.4, 0.08333333))\n",
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"\"\"\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"SELECT \n",
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"gaia.source_id, gaia.ra, gaia.dec, gaia.pmra, gaia.pmdec, best.best_neighbour_multiplicity, best.number_of_mates, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
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"FROM gaiadr2.gaia_source as gaia\n",
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"JOIN gaiadr2.panstarrs1_best_neighbour as best\n",
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" ON gaia.source_id = best.source_id\n",
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"JOIN gaiadr2.panstarrs1_original_valid as ps\n",
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" ON best.original_ext_source_id = ps.obj_id\n",
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"WHERE 1=CONTAINS(\n",
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" POINT(gaia.ra, gaia.dec),\n",
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" CIRCLE(88.8, 7.4, 0.08333333))\n",
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"\n"
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]
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}
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],
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"source": [
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"# Solution\n",
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"\"\"\"\n",
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"\n",
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"column_list = ['gaia.source_id',\n",
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" 'gaia.ra',\n",
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@@ -1139,101 +1110,9 @@
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"columns = ', '.join(column_list)\n",
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"\n",
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"query = query_base.format(columns=columns)\n",
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"print(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO: Query finished. [astroquery.utils.tap.core]\n"
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]
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}
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],
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"source": [
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"# Solution\n",
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"\n",
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"job = Gaia.launch_job_async(query=query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/html": [
|
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"<i>Table length=490</i>\n",
|
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"<table id=\"table140104380910896\" class=\"table-striped table-bordered table-condensed\">\n",
|
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"<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>best_neighbour_multiplicity</th><th>number_of_mates</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
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"<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th></th><th></th><th></th><th>mag</th></tr></thead>\n",
|
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"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int16</th><th>int16</th><th>float64</th><th>float64</th></tr></thead>\n",
|
||||
"<tr><td>3322773965056065536</td><td>88.78178020183375</td><td>7.334936530583141</td><td>0.2980633722108194</td><td>-2.5057036964736907</td><td>1</td><td>0</td><td>19.9431991577148</td><td>17.4221992492676</td></tr>\n",
|
||||
"<tr><td>3322774068134271104</td><td>88.8206092188033</td><td>7.353158142762173</td><td>-1.1065462654445488</td><td>-1.5260889445858044</td><td>1</td><td>0</td><td>18.6212005615234</td><td>16.6007995605469</td></tr>\n",
|
||||
"<tr><td>3322773930696320512</td><td>88.80843339290348</td><td>7.334853162299928</td><td>2.6074384482375215</td><td>-0.9292104395445717</td><td>1</td><td>0</td><td>--</td><td>20.2203998565674</td></tr>\n",
|
||||
"<tr><td>3322774377374425728</td><td>88.86806108182265</td><td>7.371287731275939</td><td>3.9555477866915383</td><td>-3.8676624830902435</td><td>1</td><td>0</td><td>18.0676002502441</td><td>16.9762001037598</td></tr>\n",
|
||||
"<tr><td>3322773724537891456</td><td>88.81308602813434</td><td>7.32488574492059</td><td>51.34995462741039</td><td>-33.078133430952086</td><td>1</td><td>0</td><td>20.1907005310059</td><td>17.8700008392334</td></tr>\n",
|
||||
"<tr><td>3322773724537891328</td><td>88.81570329208743</td><td>7.3223019772324855</td><td>1.9389988498951845</td><td>0.3110526931576576</td><td>1</td><td>0</td><td>22.6308002471924</td><td>19.6004009246826</td></tr>\n",
|
||||
"<tr><td>3322773930696321792</td><td>88.8050736770331</td><td>7.332371472206583</td><td>2.264014834476311</td><td>1.0772755505138008</td><td>1</td><td>0</td><td>21.2119998931885</td><td>18.3528003692627</td></tr>\n",
|
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"<tr><td>3322773724537890944</td><td>88.81241651540533</td><td>7.327864052479726</td><td>-0.36003627434304625</td><td>-6.393939291541333</td><td>1</td><td>0</td><td>20.8094005584717</td><td>18.1343002319336</td></tr>\n",
|
||||
"<tr><td>3322773930696322176</td><td>88.80128682574824</td><td>7.334292036448643</td><td>--</td><td>--</td><td>1</td><td>0</td><td>19.7306003570557</td><td>--</td></tr>\n",
|
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"<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
|
||||
"<tr><td>3322962359501481088</td><td>88.85037722908271</td><td>7.402162717053584</td><td>2.058216493648542</td><td>-2.249255322558584</td><td>1</td><td>0</td><td>17.4034996032715</td><td>15.9040002822876</td></tr>\n",
|
||||
"<tr><td>3322962393861228544</td><td>88.82108234976155</td><td>7.4044425496203</td><td>-0.916760881643629</td><td>-1.1113319053861441</td><td>1</td><td>0</td><td>--</td><td>--</td></tr>\n",
|
||||
"<tr><td>3322955831151254912</td><td>88.74620347799508</td><td>7.342728619145855</td><td>0.1559833902071379</td><td>-1.750598455959734</td><td>1</td><td>0</td><td>18.4960994720459</td><td>17.3892993927002</td></tr>\n",
|
||||
"<tr><td>3322962118983356032</td><td>88.76109637722949</td><td>7.380564308268047</td><td>--</td><td>--</td><td>1</td><td>0</td><td>18.0643997192383</td><td>16.7395000457764</td></tr>\n",
|
||||
"<tr><td>3322963527732585984</td><td>88.78813701704823</td><td>7.456696889759524</td><td>1.1363354614104264</td><td>-2.46251296961979</td><td>1</td><td>0</td><td>17.8034992218018</td><td>16.1214008331299</td></tr>\n",
|
||||
"<tr><td>3322961775385969024</td><td>88.79723215862369</td><td>7.359756552906535</td><td>2.121021366548921</td><td>-6.605711792572964</td><td>1</td><td>0</td><td>18.2070007324219</td><td>15.9947996139526</td></tr>\n",
|
||||
"<tr><td>3322962084625312512</td><td>88.78286756313868</td><td>7.384598632215225</td><td>-0.09350717810996487</td><td>1.3495903680571226</td><td>1</td><td>0</td><td>16.7978992462158</td><td>15.1180000305176</td></tr>\n",
|
||||
"<tr><td>3322962939322692608</td><td>88.73289357818679</td><td>7.407688975612043</td><td>-0.11002934783569704</td><td>1.002126813991455</td><td>1</td><td>0</td><td>17.18630027771</td><td>16.3645992279053</td></tr>\n",
|
||||
"<tr><td>3322963459013111808</td><td>88.80348931842845</td><td>7.438699901204871</td><td>0.800833828337078</td><td>-3.3780655466364626</td><td>1</td><td>0</td><td>--</td><td>16.294900894165</td></tr>\n",
|
||||
"<tr><td>3322962015904143872</td><td>88.74740822271643</td><td>7.387057037713974</td><td>-0.7201178533250112</td><td>0.5565841272341593</td><td>1</td><td>0</td><td>18.4706993103027</td><td>16.8038005828857</td></tr>\n",
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"</table>"
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],
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"text/plain": [
|
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"<Table length=490>\n",
|
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" source_id ra ... g_mean_psf_mag i_mean_psf_mag \n",
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" deg ... mag \n",
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" int64 float64 ... float64 float64 \n",
|
||||
"------------------- ----------------- ... ---------------- ----------------\n",
|
||||
"3322773965056065536 88.78178020183375 ... 19.9431991577148 17.4221992492676\n",
|
||||
"3322774068134271104 88.8206092188033 ... 18.6212005615234 16.6007995605469\n",
|
||||
"3322773930696320512 88.80843339290348 ... -- 20.2203998565674\n",
|
||||
"3322774377374425728 88.86806108182265 ... 18.0676002502441 16.9762001037598\n",
|
||||
"3322773724537891456 88.81308602813434 ... 20.1907005310059 17.8700008392334\n",
|
||||
"3322773724537891328 88.81570329208743 ... 22.6308002471924 19.6004009246826\n",
|
||||
"3322773930696321792 88.8050736770331 ... 21.2119998931885 18.3528003692627\n",
|
||||
"3322773724537890944 88.81241651540533 ... 20.8094005584717 18.1343002319336\n",
|
||||
"3322773930696322176 88.80128682574824 ... 19.7306003570557 --\n",
|
||||
" ... ... ... ... ...\n",
|
||||
"3322962359501481088 88.85037722908271 ... 17.4034996032715 15.9040002822876\n",
|
||||
"3322962393861228544 88.82108234976155 ... -- --\n",
|
||||
"3322955831151254912 88.74620347799508 ... 18.4960994720459 17.3892993927002\n",
|
||||
"3322962118983356032 88.76109637722949 ... 18.0643997192383 16.7395000457764\n",
|
||||
"3322963527732585984 88.78813701704823 ... 17.8034992218018 16.1214008331299\n",
|
||||
"3322961775385969024 88.79723215862369 ... 18.2070007324219 15.9947996139526\n",
|
||||
"3322962084625312512 88.78286756313868 ... 16.7978992462158 15.1180000305176\n",
|
||||
"3322962939322692608 88.73289357818679 ... 17.18630027771 16.3645992279053\n",
|
||||
"3322963459013111808 88.80348931842845 ... -- 16.294900894165\n",
|
||||
"3322962015904143872 88.74740822271643 ... 18.4706993103027 16.8038005828857"
|
||||
]
|
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},
|
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"execution_count": 28,
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"metadata": {},
|
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"output_type": "execute_result"
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}
|
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],
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"source": [
|
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"# Solution\n",
|
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"print(query)\n",
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"\n",
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"job = Gaia.launch_job_async(query=query)\n",
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"results = job.get_results()\n",
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"results"
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]
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@@ -1474,137 +1353,17 @@
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" POLYGON({point_list}))\n",
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" AND 1 = CONTAINS(POINT(gaia.pmra, gaia.pmdec),\n",
|
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" POLYGON({pm_point_list}))\n",
|
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"\"\"\""
|
||||
]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
|
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"name": "stdout",
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"output_type": "stream",
|
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"text": [
|
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"\n",
|
||||
"SELECT \n",
|
||||
"gaia.source_id, gaia.ra, gaia.dec, gaia.pmra, gaia.pmdec, best.best_neighbour_multiplicity, best.number_of_mates, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n",
|
||||
"FROM gaiadr2.gaia_source as gaia\n",
|
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"JOIN gaiadr2.panstarrs1_best_neighbour as best\n",
|
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" ON gaia.source_id = best.source_id\n",
|
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"JOIN gaiadr2.panstarrs1_original_valid as ps\n",
|
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" ON best.original_ext_source_id = ps.obj_id\n",
|
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"WHERE parallax < 1\n",
|
||||
" AND bp_rp BETWEEN -0.75 AND 2 \n",
|
||||
" AND 1 = CONTAINS(POINT(gaia.ra, gaia.dec), \n",
|
||||
" POLYGON(135.306, 8.39862, 126.51, 13.4449, 163.017, 54.2424, 172.933, 46.4726, 135.306, 8.39862))\n",
|
||||
" AND 1 = CONTAINS(POINT(gaia.pmra, gaia.pmdec),\n",
|
||||
" POLYGON( -4.05037121,-14.75623261, -3.41981085,-14.72365546, -3.03521988,-14.44357135, -2.26847919,-13.7140236 , -2.61172203,-13.24797471, -2.73471401,-13.09054471, -3.19923146,-12.5942653 , -3.34082546,-12.47611926, -5.67489413,-11.16083338, -5.95159272,-11.10547884, -6.42394023,-11.05981295, -7.09631023,-11.95187806, -7.30641519,-12.24559977, -7.04016696,-12.88580702, -6.00347705,-13.75912098, -4.42442296,-14.74641176))\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Solution\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"columns = ', '.join(column_list)\n",
|
||||
"\n",
|
||||
"query7 = query7_base.format(columns=columns,\n",
|
||||
" point_list=point_series['point_list'],\n",
|
||||
" pm_point_list=point_series['pm_point_list'])\n",
|
||||
"print(query7)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO: Query finished. [astroquery.utils.tap.core]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Solution\n",
|
||||
"print(query7)\n",
|
||||
"\n",
|
||||
"job = Gaia.launch_job_async(query=query7)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<i>Table length=3725</i>\n",
|
||||
"<table id=\"table140104308204640\" class=\"table-striped table-bordered table-condensed\">\n",
|
||||
"<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>pmra</th><th>pmdec</th><th>best_neighbour_multiplicity</th><th>number_of_mates</th><th>g_mean_psf_mag</th><th>i_mean_psf_mag</th></tr></thead>\n",
|
||||
"<thead><tr><th></th><th>deg</th><th>deg</th><th>mas / yr</th><th>mas / yr</th><th></th><th></th><th></th><th>mag</th></tr></thead>\n",
|
||||
"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int16</th><th>int16</th><th>float64</th><th>float64</th></tr></thead>\n",
|
||||
"<tr><td>635860218726658176</td><td>138.5187065217173</td><td>19.09233926905897</td><td>-5.941679495793577</td><td>-11.346409129876392</td><td>1</td><td>0</td><td>17.8978004455566</td><td>17.5174007415771</td></tr>\n",
|
||||
"<tr><td>635674126383965568</td><td>138.8428741026386</td><td>19.031798198627634</td><td>-3.8970011609340207</td><td>-12.702779525389634</td><td>1</td><td>0</td><td>19.2873001098633</td><td>17.6781005859375</td></tr>\n",
|
||||
"<tr><td>635535454774983040</td><td>137.8377518255436</td><td>18.864006786112604</td><td>-4.335040664412791</td><td>-14.492308604905652</td><td>1</td><td>0</td><td>16.9237995147705</td><td>16.478099822998</td></tr>\n",
|
||||
"<tr><td>635497276810313600</td><td>138.0445160213759</td><td>19.00947118796605</td><td>-7.1729306406216615</td><td>-12.291499169815987</td><td>1</td><td>0</td><td>19.9242000579834</td><td>18.3339996337891</td></tr>\n",
|
||||
"<tr><td>635614168640132864</td><td>139.59219748145836</td><td>18.807955539071433</td><td>-3.309602916796381</td><td>-13.708904908478631</td><td>1</td><td>0</td><td>16.1515998840332</td><td>14.6662998199463</td></tr>\n",
|
||||
"<tr><td>635598607974369792</td><td>139.20920023089508</td><td>18.624132868942702</td><td>-6.124445176881091</td><td>-12.833824027100611</td><td>1</td><td>0</td><td>16.5223999023438</td><td>16.1375007629395</td></tr>\n",
|
||||
"<tr><td>635737661835496576</td><td>139.93327552473934</td><td>19.167962454651423</td><td>-7.119403303682826</td><td>-12.687947497633793</td><td>1</td><td>0</td><td>14.5032997131348</td><td>13.9849004745483</td></tr>\n",
|
||||
"<tr><td>635850945892748672</td><td>139.86542888472115</td><td>20.011312663154804</td><td>-3.786655365804428</td><td>-14.28415600718206</td><td>1</td><td>0</td><td>16.5174999237061</td><td>16.0450000762939</td></tr>\n",
|
||||
"<tr><td>635600532119713664</td><td>139.22869949616816</td><td>18.685939084485494</td><td>-3.9742788217925122</td><td>-12.342426623384245</td><td>1</td><td>0</td><td>20.4505996704102</td><td>19.5177001953125</td></tr>\n",
|
||||
"<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
|
||||
"<tr><td>612241781249124608</td><td>134.3755835065194</td><td>18.129179169751275</td><td>-2.831807894848964</td><td>-13.902118573613597</td><td>1</td><td>0</td><td>20.2343997955322</td><td>18.6518001556396</td></tr>\n",
|
||||
"<tr><td>612332147361443072</td><td>134.14584721363653</td><td>18.45685585044513</td><td>-6.234287981021865</td><td>-11.500464195695072</td><td>1</td><td>0</td><td>21.3848991394043</td><td>20.3076000213623</td></tr>\n",
|
||||
"<tr><td>612426744016802432</td><td>134.68522805061076</td><td>18.77090626983678</td><td>-3.7691372464459554</td><td>-12.889167493118862</td><td>1</td><td>0</td><td>17.8281002044678</td><td>17.4281005859375</td></tr>\n",
|
||||
"<tr><td>612331739340341760</td><td>134.12176196902254</td><td>18.42768872157865</td><td>-3.9894012386388735</td><td>-12.60504410507441</td><td>1</td><td>0</td><td>21.8656997680664</td><td>19.5223007202148</td></tr>\n",
|
||||
"<tr><td>612282738058264960</td><td>134.0445768189235</td><td>18.11915820167003</td><td>-2.5972485319419127</td><td>-13.651740929272187</td><td>1</td><td>0</td><td>22.5151996612549</td><td>19.9743995666504</td></tr>\n",
|
||||
"<tr><td>612386332668697600</td><td>135.45701048323093</td><td>18.63266345155342</td><td>-5.07684899854408</td><td>-12.436641304786672</td><td>1</td><td>0</td><td>19.3792991638184</td><td>17.9923000335693</td></tr>\n",
|
||||
"<tr><td>612296172717818624</td><td>133.80060286960668</td><td>18.08186533343457</td><td>-6.112792578821885</td><td>-12.50750861370402</td><td>1</td><td>0</td><td>17.4944000244141</td><td>16.926700592041</td></tr>\n",
|
||||
"<tr><td>612250375480101760</td><td>134.64754712466774</td><td>18.122419425065015</td><td>-2.8969262278467127</td><td>-14.061676353845487</td><td>1</td><td>0</td><td>15.3330001831055</td><td>14.6280002593994</td></tr>\n",
|
||||
"<tr><td>612394926899159168</td><td>135.51997060013844</td><td>18.817675531233004</td><td>-3.9968965218753763</td><td>-13.526821099431533</td><td>1</td><td>0</td><td>16.4414005279541</td><td>15.8212003707886</td></tr>\n",
|
||||
"<tr><td>612256418500423168</td><td>134.90752972739924</td><td>18.280596648172743</td><td>-6.109836304219565</td><td>-12.145212331165776</td><td>1</td><td>0</td><td>20.8715991973877</td><td>19.9612007141113</td></tr>\n",
|
||||
"</table>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<Table length=3725>\n",
|
||||
" source_id ra ... g_mean_psf_mag i_mean_psf_mag \n",
|
||||
" deg ... mag \n",
|
||||
" int64 float64 ... float64 float64 \n",
|
||||
"------------------ ------------------ ... ---------------- ----------------\n",
|
||||
"635860218726658176 138.5187065217173 ... 17.8978004455566 17.5174007415771\n",
|
||||
"635674126383965568 138.8428741026386 ... 19.2873001098633 17.6781005859375\n",
|
||||
"635535454774983040 137.8377518255436 ... 16.9237995147705 16.478099822998\n",
|
||||
"635497276810313600 138.0445160213759 ... 19.9242000579834 18.3339996337891\n",
|
||||
"635614168640132864 139.59219748145836 ... 16.1515998840332 14.6662998199463\n",
|
||||
"635598607974369792 139.20920023089508 ... 16.5223999023438 16.1375007629395\n",
|
||||
"635737661835496576 139.93327552473934 ... 14.5032997131348 13.9849004745483\n",
|
||||
"635850945892748672 139.86542888472115 ... 16.5174999237061 16.0450000762939\n",
|
||||
"635600532119713664 139.22869949616816 ... 20.4505996704102 19.5177001953125\n",
|
||||
" ... ... ... ... ...\n",
|
||||
"612241781249124608 134.3755835065194 ... 20.2343997955322 18.6518001556396\n",
|
||||
"612332147361443072 134.14584721363653 ... 21.3848991394043 20.3076000213623\n",
|
||||
"612426744016802432 134.68522805061076 ... 17.8281002044678 17.4281005859375\n",
|
||||
"612331739340341760 134.12176196902254 ... 21.8656997680664 19.5223007202148\n",
|
||||
"612282738058264960 134.0445768189235 ... 22.5151996612549 19.9743995666504\n",
|
||||
"612386332668697600 135.45701048323093 ... 19.3792991638184 17.9923000335693\n",
|
||||
"612296172717818624 133.80060286960668 ... 17.4944000244141 16.926700592041\n",
|
||||
"612250375480101760 134.64754712466774 ... 15.3330001831055 14.6280002593994\n",
|
||||
"612394926899159168 135.51997060013844 ... 16.4414005279541 15.8212003707886\n",
|
||||
"612256418500423168 134.90752972739924 ... 20.8715991973877 19.9612007141113"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Solution\n",
|
||||
"\n",
|
||||
"job = Gaia.launch_job_async(query=query7)\n",
|
||||
"results = job.get_results()\n",
|
||||
"results"
|
||||
]
|
||||
|
||||
@@ -10,23 +10,21 @@
|
||||
"exercises: 0\n",
|
||||
"questions:\n",
|
||||
"\n",
|
||||
"- \"How do we use Matplotlib to select a polygon and Pandas to merge data from multiple tables?\"\n",
|
||||
"- \"How do we use Matplotlib to define a polygon and select points that fall inside it?\"\n",
|
||||
"\n",
|
||||
"objectives:\n",
|
||||
"\n",
|
||||
"- \"Use Matplotlib to specify a polygon and determine which points fall inside it.\"\n",
|
||||
"- \"Use isochrone data to specify a polygon and determine which points fall inside it.\"\n",
|
||||
"\n",
|
||||
"- \"Use Pandas to merge data from multiple `DataFrames`, much like a database `JOIN` operation.\"\n",
|
||||
"- \"Use Matplotlib features to customize the appearance of figures.\"\n",
|
||||
"\n",
|
||||
"keypoints:\n",
|
||||
"\n",
|
||||
"- \"Matplotlib provides operations for working with points, polygons, and other geometric entities, so it's not just for making figures.\"\n",
|
||||
"\n",
|
||||
"- \"If you want to perform something like a database `JOIN` operation with data that is in a Pandas `DataFrame`, you can use the `join` or `merge` function. In many cases, `merge` is easier to use because the arguments are more like SQL.\"\n",
|
||||
"\n",
|
||||
"- \"Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret.\"\n",
|
||||
"\n",
|
||||
"- \"Be sure to record every element of the data analysis pipeline that would be needed to replicate the results.\"\n",
|
||||
"- \"Record every element of the data analysis pipeline that would be needed to replicate the results.\"\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"\n",
|
||||
|
||||
@@ -22,9 +22,9 @@
|
||||
"\n",
|
||||
"keypoints:\n",
|
||||
"\n",
|
||||
"- \"The most effective figures focus on telling a single story clearly and compellingly.\"\n",
|
||||
"- \"The most effective figures focus on telling a single story clearly.\"\n",
|
||||
"\n",
|
||||
"- \"Consider using annotations to guide the readers attention to the most important elements of a figure.\"\n",
|
||||
"- \"Consider using annotations to guide the reader's attention to the most important elements of a figure.\"\n",
|
||||
"\n",
|
||||
"- \"The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.\"\n",
|
||||
"\n",
|
||||
@@ -1265,7 +1265,7 @@
|
||||
"\n",
|
||||
"* The most effective figures focus on telling a single story clearly and compellingly.\n",
|
||||
"\n",
|
||||
"* Consider using annotations to guide the readers attention to the most important elements of a figure.\n",
|
||||
"* Consider using annotations to guide the reader's attention to the most important elements of a figure.\n",
|
||||
"\n",
|
||||
"* The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.\n",
|
||||
"\n",
|
||||
|
||||
Reference in New Issue
Block a user