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Applying Functions to Multiple Columns · cyrusso/python_reference@db6679e · GitHub

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Applying Functions to Multiple Columns
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‎tutorials/things_in_pandas.ipynb‎

Lines changed: 87 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
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{
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"metadata": {
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"name": "",
4-
"signature": "sha256:c69dee8958d58e899a12b80810cc37f7abd7a90f9b76135251a76499ed8aeb2a"
4+
"signature": "sha256:3155cd3fa2449393a467f91f3cdbb32eeac212db664843ef30f96b635dbfc06d"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
@@ -1722,7 +1722,7 @@
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"input": [
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"# Filling cells with data\n",
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"\n",
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"df.loc[df.index[-1], 'player'] = 'New Player'\n",
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"df.loc[df.index[-1], 'player'] = 'new player'\n",
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"df.loc[df.index[-1], 'salary'] = 12.3\n",
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"df.tail(3)"
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],
@@ -1777,7 +1777,7 @@
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td> New Player</td>\n",
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" <td> new player</td>\n",
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" <td> 12.3</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
@@ -1799,7 +1799,7 @@
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" player salary games goals assists shots_on_target \\\n",
18001800
"8 saido berahino 13.8 21 9 0 20 \n",
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"9 steven gerrard 13.8 20 5 1 11 \n",
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"10 New Player 12.3 NaN NaN NaN NaN \n",
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"10 new player 12.3 NaN NaN NaN NaN \n",
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"\n",
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" points_per_game points position team \n",
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"8 7.02 147.43 forward west brom \n",
@@ -2548,7 +2548,7 @@
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"input": [
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"# Selecting only those players that either playing for Arsenal or Chelsea\n",
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"\n",
2551-
"df[ (df['team'] == 'Arsenal') | (df['team'] == 'Chelsea') ]"
2551+
"df[ (df['team'] == 'arsenal') | (df['team'] == 'chelsea') ]"
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],
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"language": "python",
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"metadata": {},
@@ -2573,6 +2573,58 @@
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
2577+
" <th>1</th>\n",
2578+
" <td> alexis s\u00e1nchez</td>\n",
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" <td> 15</td>\n",
2580+
" <td> 0</td>\n",
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" <td> 12</td>\n",
2582+
" <td> 7</td>\n",
2583+
" <td> 29</td>\n",
2584+
" <td> 11.19</td>\n",
2585+
" <td> 223.86</td>\n",
2586+
" <td> forward</td>\n",
2587+
" <td> arsenal</td>\n",
2588+
" </tr>\n",
2589+
" <tr>\n",
2590+
" <th>3</th>\n",
2591+
" <td> eden hazard</td>\n",
2592+
" <td> 18.9</td>\n",
2593+
" <td> 21</td>\n",
2594+
" <td> 8</td>\n",
2595+
" <td> 4</td>\n",
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" <td> 17</td>\n",
2597+
" <td> 13.05</td>\n",
2598+
" <td> 274.04</td>\n",
2599+
" <td> midfield</td>\n",
2600+
" <td> chelsea</td>\n",
2601+
" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td> santiago cazorla</td>\n",
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" <td> 14.8</td>\n",
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" <td> 20</td>\n",
2607+
" <td> 4</td>\n",
2608+
" <td> 0</td>\n",
2609+
" <td> 20</td>\n",
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" <td> 9.97</td>\n",
2611+
" <td> 0.00</td>\n",
2612+
" <td> midfield</td>\n",
2613+
" <td> arsenal</td>\n",
2614+
" </tr>\n",
2615+
" <tr>\n",
2616+
" <th>9</th>\n",
2617+
" <td> cesc f\u00e0bregas</td>\n",
2618+
" <td> 14.0</td>\n",
2619+
" <td> 20</td>\n",
2620+
" <td> 2</td>\n",
2621+
" <td> 14</td>\n",
2622+
" <td> 10</td>\n",
2623+
" <td> 10.47</td>\n",
2624+
" <td> 209.49</td>\n",
2625+
" <td> midfield</td>\n",
2626+
" <td> chelsea</td>\n",
2627+
" </tr>\n",
25762628
" </tbody>\n",
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"</table>\n",
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"</div>"
@@ -2581,9 +2633,17 @@
25812633
"output_type": "pyout",
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"prompt_number": 21,
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"text": [
2584-
"Empty DataFrame\n",
2585-
"Columns: [player, salary, games, goals, assists, shots_on_target, points_per_game, points, position, team]\n",
2586-
"Index: []"
2636+
" player salary games goals assists shots_on_target \\\n",
2637+
"1 alexis s\u00e1nchez 15 0 12 7 29 \n",
2638+
"3 eden hazard 18.9 21 8 4 17 \n",
2639+
"7 santiago cazorla 14.8 20 4 0 20 \n",
2640+
"9 cesc f\u00e0bregas 14.0 20 2 14 10 \n",
2641+
"\n",
2642+
" points_per_game points position team \n",
2643+
"1 11.19 223.86 forward arsenal \n",
2644+
"3 13.05 274.04 midfield chelsea \n",
2645+
"7 9.97 0.00 midfield arsenal \n",
2646+
"9 10.47 209.49 midfield chelsea "
25872647
]
25882648
}
25892649
],
@@ -2595,7 +2655,7 @@
25952655
"input": [
25962656
"# Selecting forwards from Arsenal only\n",
25972657
"\n",
2598-
"df[ (df['team'] == 'Arsenal') & (df['position'] == 'Forward') ]"
2658+
"df[ (df['team'] == 'arsenal') & (df['position'] == 'forward') ]"
25992659
],
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"language": "python",
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"metadata": {},
@@ -2620,6 +2680,19 @@
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
2683+
" <tr>\n",
2684+
" <th>1</th>\n",
2685+
" <td> alexis s\u00e1nchez</td>\n",
2686+
" <td> 15</td>\n",
2687+
" <td> 0</td>\n",
2688+
" <td> 12</td>\n",
2689+
" <td> 7</td>\n",
2690+
" <td> 29</td>\n",
2691+
" <td> 11.19</td>\n",
2692+
" <td> 223.86</td>\n",
2693+
" <td> forward</td>\n",
2694+
" <td> arsenal</td>\n",
2695+
" </tr>\n",
26232696
" </tbody>\n",
26242697
"</table>\n",
26252698
"</div>"
@@ -2628,9 +2701,11 @@
26282701
"output_type": "pyout",
26292702
"prompt_number": 22,
26302703
"text": [
2631-
"Empty DataFrame\n",
2632-
"Columns: [player, salary, games, goals, assists, shots_on_target, points_per_game, points, position, team]\n",
2633-
"Index: []"
2704+
" player salary games goals assists shots_on_target \\\n",
2705+
"1 alexis s\u00e1nchez 15 0 12 7 29 \n",
2706+
"\n",
2707+
" points_per_game points position team \n",
2708+
"1 11.19 223.86 forward arsenal "
26342709
]
26352710
}
26362711
],

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