|
15 | 15 | ] |
16 | 16 | }, |
17 | 17 | { |
18 | | - "attachments": {}, |
19 | 18 | "cell_type": "markdown", |
20 | 19 | "metadata": {}, |
21 | 20 | "source": [ |
|
117 | 116 | ] |
118 | 117 | }, |
119 | 118 | { |
120 | | - "attachments": {}, |
121 | 119 | "cell_type": "markdown", |
122 | 120 | "metadata": {}, |
123 | 121 | "source": [ |
|
197 | 195 | ] |
198 | 196 | }, |
199 | 197 | { |
200 | | - "attachments": {}, |
201 | 198 | "cell_type": "markdown", |
202 | 199 | "metadata": {}, |
203 | 200 | "source": [ |
|
206 | 203 | ] |
207 | 204 | }, |
208 | 205 | { |
209 | | - "attachments": {}, |
210 | 206 | "cell_type": "markdown", |
211 | 207 | "metadata": {}, |
212 | 208 | "source": [ |
|
215 | 211 | ] |
216 | 212 | }, |
217 | 213 | { |
218 | | - "attachments": {}, |
219 | 214 | "cell_type": "markdown", |
220 | 215 | "metadata": {}, |
221 | 216 | "source": [ |
|
239 | 234 | ] |
240 | 235 | }, |
241 | 236 | { |
242 | | - "attachments": {}, |
243 | 237 | "cell_type": "markdown", |
244 | 238 | "metadata": {}, |
245 | 239 | "source": [ |
|
248 | 242 | ] |
249 | 243 | }, |
250 | 244 | { |
251 | | - "attachments": {}, |
252 | 245 | "cell_type": "markdown", |
253 | 246 | "metadata": {}, |
254 | 247 | "source": [ |
|
257 | 250 | ] |
258 | 251 | }, |
259 | 252 | { |
260 | | - "attachments": {}, |
261 | 253 | "cell_type": "markdown", |
262 | 254 | "metadata": {}, |
263 | 255 | "source": [ |
|
281 | 273 | ] |
282 | 274 | }, |
283 | 275 | { |
284 | | - "attachments": {}, |
285 | 276 | "cell_type": "markdown", |
286 | 277 | "metadata": {}, |
287 | 278 | "source": [ |
|
317 | 308 | ] |
318 | 309 | }, |
319 | 310 | { |
320 | | - "attachments": {}, |
321 | 311 | "cell_type": "markdown", |
322 | 312 | "metadata": {}, |
323 | 313 | "source": [ |
|
332 | 322 | ] |
333 | 323 | }, |
334 | 324 | { |
335 | | - "attachments": {}, |
336 | 325 | "cell_type": "markdown", |
337 | 326 | "metadata": {}, |
338 | 327 | "source": [ |
|
442 | 431 | " \n" |
443 | 432 | ] |
444 | 433 | }, |
| 434 | + { |
| 435 | + "cell_type": "markdown", |
| 436 | + "metadata": {}, |
| 437 | + "source": [ |
| 438 | + "## 15 Latest Pandas Features – What Makes Python Pandas Unique?" |
| 439 | + ] |
| 440 | + }, |
| 441 | + { |
| 442 | + "cell_type": "markdown", |
| 443 | + "metadata": {}, |
| 444 | + "source": [ |
| 445 | + "" |
| 446 | + ] |
| 447 | + }, |
| 448 | + { |
| 449 | + "cell_type": "markdown", |
| 450 | + "metadata": {}, |
| 451 | + "source": [ |
| 452 | + "#### 1. Handling of data" |
| 453 | + ] |
| 454 | + }, |
| 455 | + { |
| 456 | + "cell_type": "markdown", |
| 457 | + "metadata": {}, |
| 458 | + "source": [ |
| 459 | + "The Pandas library provides a really fast and efficient way to manage and explore data. It does that by providing us with Series and DataFrames, which help us not only to represent data efficiently but also manipulate it in various ways. These features of Pandas is exactly what makes it such an attractive library for data scientists." |
| 460 | + ] |
| 461 | + }, |
| 462 | + { |
| 463 | + "cell_type": "markdown", |
| 464 | + "metadata": {}, |
| 465 | + "source": [ |
| 466 | + "#### 2. Alignment and indexing" |
| 467 | + ] |
| 468 | + }, |
| 469 | + { |
| 470 | + "cell_type": "markdown", |
| 471 | + "metadata": {}, |
| 472 | + "source": [ |
| 473 | + "Having data is useless if you don’t know where it belongs and what it tells us about. Therefore, labeling of data is of utmost importance. Another important factor is an organization, without which data would be impossible to read. These two needs: Organization and labeling of data are perfectly taken care of by the intelligent methods of alignment and indexing, which can be found within Pandas." |
| 474 | + ] |
| 475 | + }, |
| 476 | + { |
| 477 | + "cell_type": "markdown", |
| 478 | + "metadata": {}, |
| 479 | + "source": [ |
| 480 | + "#### 3. Handling missing data" |
| 481 | + ] |
| 482 | + }, |
| 483 | + { |
| 484 | + "cell_type": "markdown", |
| 485 | + "metadata": {}, |
| 486 | + "source": [ |
| 487 | + " Data can be quite confusing to read. But that is not even one of the major problems. Data is very crude in nature and one of the many problems associated with data is the occurrence of missing data or value. Therefore, it is pertinent to handle the missing values properly so that they do not adulterate our study results. Some Pandas features have you covered on this end because handling missing values is integrated within the library." |
| 488 | + ] |
| 489 | + }, |
| 490 | + { |
| 491 | + "cell_type": "markdown", |
| 492 | + "metadata": {}, |
| 493 | + "source": [ |
| 494 | + "#### 4. Cleaning up data" |
| 495 | + ] |
| 496 | + }, |
| 497 | + { |
| 498 | + "cell_type": "markdown", |
| 499 | + "metadata": {}, |
| 500 | + "source": [ |
| 501 | + "Data can be very crude. Therefore it is really messy, so much so that performing any analysis over such data would lead to severely wrong results. Thus it is of extreme importance that we clean our data up, and this Pandas feature is easily provided. They help a lot to not only make the code clean but also tidies up the data so that even the normal eye can decipher parts of the data. The cleaner the data, the better the result." |
| 502 | + ] |
| 503 | + }, |
| 504 | + { |
| 505 | + "cell_type": "markdown", |
| 506 | + "metadata": {}, |
| 507 | + "source": [ |
| 508 | + "#### 5. Input and output tools" |
| 509 | + ] |
| 510 | + }, |
| 511 | + { |
| 512 | + "cell_type": "markdown", |
| 513 | + "metadata": {}, |
| 514 | + "source": [ |
| 515 | + "Pandas provide a wide array of built-in tools for the purpose of reading and writing data. While analyzing you will obviously need to read and write data into data structures, web service, databases, etc. This has been made extremely simple with the help of Pandas’ inbuilt tools. In other languages, it would probably take a lot of code to generate the same results, which would only slow down the process of analyzing." |
| 516 | + ] |
| 517 | + }, |
| 518 | + { |
| 519 | + "cell_type": "markdown", |
| 520 | + "metadata": {}, |
| 521 | + "source": [ |
| 522 | + "#### 6. Multiple file formats supported" |
| 523 | + ] |
| 524 | + }, |
| 525 | + { |
| 526 | + "cell_type": "markdown", |
| 527 | + "metadata": {}, |
| 528 | + "source": [ |
| 529 | + "Data these days can be found in so many different file formats, that it becomes crucial that libraries used for data analysis can read various file formats. Pandas aces this sector with a huge scope of file formats supported. Whether it is a JSON or CSV, Pandas can support it all, including Excel and HDF5. This can be considered as one of the most appealing Python Pandas features." |
| 530 | + ] |
| 531 | + }, |
| 532 | + { |
| 533 | + "cell_type": "markdown", |
| 534 | + "metadata": {}, |
| 535 | + "source": [ |
| 536 | + "#### 7. Merging and joining of datasets" |
| 537 | + ] |
| 538 | + }, |
| 539 | + { |
| 540 | + "cell_type": "markdown", |
| 541 | + "metadata": {}, |
| 542 | + "source": [ |
| 543 | + "While analyzing data we constantly need to merge and join multiple datasets to create a final dataset to be able to properly analyze it. This is important because if the datasets aren’t merged or joined properly, then it is going to affect the results adversely and we do not want that. Pandas can help to merge various datasets, with extreme efficiency so that we don’t face any problems while analyzing the data." |
| 544 | + ] |
| 545 | + }, |
| 546 | + { |
| 547 | + "cell_type": "markdown", |
| 548 | + "metadata": {}, |
| 549 | + "source": [ |
| 550 | + "#### 8. A lot of time series" |
| 551 | + ] |
| 552 | + }, |
| 553 | + { |
| 554 | + "cell_type": "markdown", |
| 555 | + "metadata": {}, |
| 556 | + "source": [ |
| 557 | + "These Pandas features won’t make sense to beginners right away, but they will be of great use in the future. These features include the likes of moving window statistics and frequency conversion. So, as we go deeper into learning Pandas we will see how essential and useful these features are, for a data scientist." |
| 558 | + ] |
| 559 | + }, |
| 560 | + { |
| 561 | + "cell_type": "markdown", |
| 562 | + "metadata": {}, |
| 563 | + "source": [ |
| 564 | + "#### 9. Optimized performance" |
| 565 | + ] |
| 566 | + }, |
| 567 | + { |
| 568 | + "cell_type": "markdown", |
| 569 | + "metadata": {}, |
| 570 | + "source": [ |
| 571 | + "Pandas is said to have a really optimized performance, which makes it really fast and suitable for data science. The critical code for Pandas is written in C or Cython, which makes it extremely responsive and fast." |
| 572 | + ] |
| 573 | + }, |
| 574 | + { |
| 575 | + "cell_type": "markdown", |
| 576 | + "metadata": {}, |
| 577 | + "source": [ |
| 578 | + "#### 10. Python support" |
| 579 | + ] |
| 580 | + }, |
| 581 | + { |
| 582 | + "cell_type": "markdown", |
| 583 | + "metadata": {}, |
| 584 | + "source": [ |
| 585 | + "This feature of Pandas is the deal closer. With an insane amount of helpful libraries at your, disposal Python has become one of the most sought after programming languages for data analysis. Thus Pandas being a part of Python and allowing us to access the other libraries like NumPy and MatPlotLib" |
| 586 | + ] |
| 587 | + }, |
| 588 | + { |
| 589 | + "cell_type": "markdown", |
| 590 | + "metadata": {}, |
| 591 | + "source": [ |
| 592 | + "#### 11. Visualize" |
| 593 | + ] |
| 594 | + }, |
| 595 | + { |
| 596 | + "cell_type": "markdown", |
| 597 | + "metadata": {}, |
| 598 | + "source": [ |
| 599 | + "Visualizing the data is an important part of data science. It is what make the results of the study understandable by human eyes. Pandas have an in-built ability to help you plot your data and see the various kinds of graphs formed. Without visualization, data analysis would make no sense to most of the population." |
| 600 | + ] |
| 601 | + }, |
| 602 | + { |
| 603 | + "cell_type": "markdown", |
| 604 | + "metadata": {}, |
| 605 | + "source": [ |
| 606 | + "#### 12. Grouping" |
| 607 | + ] |
| 608 | + }, |
| 609 | + { |
| 610 | + "cell_type": "markdown", |
| 611 | + "metadata": {}, |
| 612 | + "source": [ |
| 613 | + "Having the ability to separate your data and grouping it according to the criteria you want, is pretty essential. With the help of the features of Pandas like GroupBy, you can split data into categories of your choice, according to the criteria you set. The GroupBy function splits the data, implements a function and then combines the results." |
| 614 | + ] |
| 615 | + }, |
| 616 | + { |
| 617 | + "cell_type": "markdown", |
| 618 | + "metadata": {}, |
| 619 | + "source": [ |
| 620 | + "#### 13. Mask data" |
| 621 | + ] |
| 622 | + }, |
| 623 | + { |
| 624 | + "cell_type": "markdown", |
| 625 | + "metadata": {}, |
| 626 | + "source": [ |
| 627 | + "Sometimes, certain data is not needed for analysis of data and thus it is important that you filter your data according to the things you want from it. Using the mask function in Pandas allows you exactly to do that. It is extremely useful since whenever it finds data which meets the criteria you set for elimination, it turns the data into a missing value." |
| 628 | + ] |
| 629 | + }, |
| 630 | + { |
| 631 | + "cell_type": "markdown", |
| 632 | + "metadata": {}, |
| 633 | + "source": [ |
| 634 | + "#### 14. Unique data" |
| 635 | + ] |
| 636 | + }, |
| 637 | + { |
| 638 | + "cell_type": "markdown", |
| 639 | + "metadata": {}, |
| 640 | + "source": [ |
| 641 | + "Data always has a lot of repetition, therefore it is important that you are able to analyze data which has only unique values. This is present in the Python Pandas features and lets the user see the unique values in the dataset with the function dataset.column.unique(). Where “dataset” and “column” are the names of your dataset and column, respectively." |
| 642 | + ] |
| 643 | + }, |
| 644 | + { |
| 645 | + "cell_type": "markdown", |
| 646 | + "metadata": {}, |
| 647 | + "source": [ |
| 648 | + "#### 15. Perform mathematical operations on the data" |
| 649 | + ] |
| 650 | + }, |
| 651 | + { |
| 652 | + "cell_type": "markdown", |
| 653 | + "metadata": {}, |
| 654 | + "source": [ |
| 655 | + "The apply function in Pandas allows you to implement a mathematical operation on the data. This helps enormously, because sometimes the dataset you have, is just not of the correct order. This will be correct by simply using a mathematical operation on the dataset. This is one of the most attractive features of Pandas." |
| 656 | + ] |
| 657 | + }, |
| 658 | + { |
| 659 | + "cell_type": "code", |
| 660 | + "execution_count": null, |
| 661 | + "metadata": {}, |
| 662 | + "outputs": [], |
| 663 | + "source": [] |
| 664 | + }, |
| 665 | + { |
| 666 | + "cell_type": "code", |
| 667 | + "execution_count": null, |
| 668 | + "metadata": {}, |
| 669 | + "outputs": [], |
| 670 | + "source": [] |
| 671 | + }, |
| 672 | + { |
| 673 | + "cell_type": "code", |
| 674 | + "execution_count": null, |
| 675 | + "metadata": {}, |
| 676 | + "outputs": [], |
| 677 | + "source": [] |
| 678 | + }, |
| 679 | + { |
| 680 | + "cell_type": "code", |
| 681 | + "execution_count": null, |
| 682 | + "metadata": {}, |
| 683 | + "outputs": [], |
| 684 | + "source": [] |
| 685 | + }, |
| 686 | + { |
| 687 | + "cell_type": "code", |
| 688 | + "execution_count": null, |
| 689 | + "metadata": {}, |
| 690 | + "outputs": [], |
| 691 | + "source": [] |
| 692 | + }, |
| 693 | + { |
| 694 | + "cell_type": "code", |
| 695 | + "execution_count": null, |
| 696 | + "metadata": {}, |
| 697 | + "outputs": [], |
| 698 | + "source": [] |
| 699 | + }, |
445 | 700 | { |
446 | 701 | "cell_type": "code", |
447 | 702 | "execution_count": null, |
|
0 commit comments