├── Data Cleaning -Working with Column Names ├── Data Cleaning :Working with Column Names .ipynb └── raw_dataset.csv ├── Data Cleaning in Julia Practical Example.ipynb ├── Data Cleaning in Python Practical Examples.ipynb ├── Data_Cleaning_In_Python_Working_with_Str ├── Data Cleaning (Working with .str ) Practical Examples 3 .ipynb └── dataset.csv ├── Julia - Reading Most Commonly Used File formats in DataScience with Julia ├── .ipynb_checkpoints │ └── Most Common Used File Format In Julia-checkpoint.ipynb ├── Reading the Most Commonly Used File Format In Julia.ipynb ├── julialogo.png ├── myvalidjsonfile.json ├── testfile.csv ├── testfile.docx.docx ├── testfile.h5 ├── testfile.hdf5 ├── testfile.json ├── testfile.rdf ├── testfile.txt ├── testfile.xml ├── testfile.zip ├── testfileexcel.xlsx ├── testfiletab.txt ├── testfiletab.xlsx └── testhtml.html ├── README.md ├── Reading Most Commonly Used File Format in DataScience with Python ├── Most Commonly Used File Formats In DataScience.ipynb ├── examplefile.csv ├── examplefile.docx.docx ├── examplefile.h5 ├── examplefile.hdf5 ├── examplefile.json ├── examplefile.rdf ├── examplefile.txt ├── examplefile.xml ├── examplefile.zip ├── examplefileexcel.xlsx ├── examplefiletab.txt ├── examplefiletab.xlsx ├── examplehtml.html ├── myexample.json ├── myvalidjsonfile.json └── sampleh5files │ ├── my27.h5 │ ├── my46.h5 │ ├── my50.h5 │ ├── my57.h5 │ ├── my59.h5 │ ├── my62.h5 │ ├── my63.h5 │ ├── my71.h5 │ ├── my87.h5 │ └── my97.h5 ├── raw_data_unmodified.csv ├── unclean_data.csv ├── unclean_data1.csv └── unclean_data_unmodified.csv /Data Cleaning -Working with Column Names/Data Cleaning :Working with Column Names .ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Data Cleaning Practical Examples\n", 8 | "#### Working With Columns Names\n", 9 | "\n", 10 | "+ Get Random Dataset here https://www.generatedata.com/\n", 11 | "\n", 12 | "#### Outline\n", 13 | " + How to check columns\n", 14 | " + How to rename columns\n", 15 | " + How to put underscore in all columns\n", 16 | " + How to replace a character or empty space in column names\n", 17 | " + How to uppercase/lowercase columns\n", 18 | " + How to select all columns except one\n", 19 | " + How to select columns of a particular order or phrase(df.filter)\n", 20 | " + How to select a group of column name" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 1, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "# Load Dataset\n", 30 | "import pandas as pd" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 2, 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "# Load Dataset\n", 40 | "df = pd.read_csv(\"raw_dataset.csv\")" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 3, 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "data": { 50 | "text/html": [ 51 | "
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First NameLast nameAgeSALARYSTREET Address1STREET Address2STREET Address3email
0JoelPadilla10/28/2019$92.32431-6530 Eu, Rd.364-2264 Augue Rd.P.O. Box 864, 3882 Orci Streeteu@nibh.com
1FritzTyler09/27/2019$83.91Ap #377-2267 Ac Av.979-2228 Vel Ave9865 Eu Av.est.ac.mattis@malesuadafringilla.net
2WingPhelps02/18/2019$17.15Ap #545-5786 Pulvinar AveAp #973-5781 Sagittis Avenue9959 Ut St.dolor@cubilia.net
3RyanRoss05/21/2019$45.97634-7858 Id Road907-8824 Fringilla Ave318-5271 In Aveinterdum.libero.dui@vitaeerat.com
4DrakeDay01/09/2020$84.38999-8221 Tempor, St.297-6939 Turpis. AveP.O. Box 638, 6932 Laoreet Rd.nulla.Integer.vulputate@liberoat.ca
\n", 137 | "
" 138 | ], 139 | "text/plain": [ 140 | " First Name Last name Age SALARY STREET Address1 \\\n", 141 | "0 Joel Padilla 10/28/2019 $92.32 431-6530 Eu, Rd. \n", 142 | "1 Fritz Tyler 09/27/2019 $83.91 Ap #377-2267 Ac Av. \n", 143 | "2 Wing Phelps 02/18/2019 $17.15 Ap #545-5786 Pulvinar Ave \n", 144 | "3 Ryan Ross 05/21/2019 $45.97 634-7858 Id Road \n", 145 | "4 Drake Day 01/09/2020 $84.38 999-8221 Tempor, St. \n", 146 | "\n", 147 | " STREET Address2 STREET Address3 \\\n", 148 | "0 364-2264 Augue Rd. P.O. Box 864, 3882 Orci Street \n", 149 | "1 979-2228 Vel Ave 9865 Eu Av. \n", 150 | "2 Ap #973-5781 Sagittis Avenue 9959 Ut St. \n", 151 | "3 907-8824 Fringilla Ave 318-5271 In Ave \n", 152 | "4 297-6939 Turpis. Ave P.O. Box 638, 6932 Laoreet Rd. \n", 153 | "\n", 154 | " email \n", 155 | "0 eu@nibh.com \n", 156 | "1 est.ac.mattis@malesuadafringilla.net \n", 157 | "2 dolor@cubilia.net \n", 158 | "3 interdum.libero.dui@vitaeerat.com \n", 159 | "4 nulla.Integer.vulputate@liberoat.ca " 160 | ] 161 | }, 162 | "execution_count": 3, 163 | "metadata": {}, 164 | "output_type": "execute_result" 165 | } 166 | ], 167 | "source": [ 168 | "# Firt Rows\n", 169 | "df.head()" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 4, 175 | "metadata": {}, 176 | "outputs": [ 177 | { 178 | "data": { 179 | "text/plain": [ 180 | "Index(['First Name', 'Last name', 'Age', 'SALARY', 'STREET Address1',\n", 181 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 182 | " dtype='object')" 183 | ] 184 | }, 185 | "execution_count": 4, 186 | "metadata": {}, 187 | "output_type": "execute_result" 188 | } 189 | ], 190 | "source": [ 191 | "# Columns\n", 192 | "df.columns" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 5, 198 | "metadata": {}, 199 | "outputs": [ 200 | { 201 | "data": { 202 | "text/plain": [ 203 | "['T',\n", 204 | " '__abs__',\n", 205 | " '__add__',\n", 206 | " '__and__',\n", 207 | " '__array__',\n", 208 | " '__array_priority__',\n", 209 | " '__array_wrap__',\n", 210 | " '__bool__',\n", 211 | " '__bytes__',\n", 212 | " '__class__',\n", 213 | " '__contains__',\n", 214 | " '__copy__',\n", 215 | " '__deepcopy__',\n", 216 | " '__delattr__',\n", 217 | " '__dict__',\n", 218 | " '__dir__',\n", 219 | " '__divmod__',\n", 220 | " '__doc__',\n", 221 | " '__eq__',\n", 222 | " '__floordiv__',\n", 223 | " '__format__',\n", 224 | " '__ge__',\n", 225 | " '__getattribute__',\n", 226 | " '__getitem__',\n", 227 | " '__gt__',\n", 228 | " '__hash__',\n", 229 | " '__iadd__',\n", 230 | " '__init__',\n", 231 | " '__init_subclass__',\n", 232 | " '__inv__',\n", 233 | " '__iter__',\n", 234 | " '__le__',\n", 235 | " '__len__',\n", 236 | " '__lt__',\n", 237 | " '__mod__',\n", 238 | " '__module__',\n", 239 | " '__mul__',\n", 240 | " '__ne__',\n", 241 | " '__neg__',\n", 242 | " '__new__',\n", 243 | " '__nonzero__',\n", 244 | " '__or__',\n", 245 | " '__pos__',\n", 246 | " '__pow__',\n", 247 | " '__radd__',\n", 248 | " '__reduce__',\n", 249 | " '__reduce_ex__',\n", 250 | " '__repr__',\n", 251 | " '__rfloordiv__',\n", 252 | " '__rmul__',\n", 253 | " '__rpow__',\n", 254 | " '__rsub__',\n", 255 | " '__rtruediv__',\n", 256 | " '__setattr__',\n", 257 | " '__setitem__',\n", 258 | " '__setstate__',\n", 259 | " '__sizeof__',\n", 260 | " '__str__',\n", 261 | " '__sub__',\n", 262 | " '__subclasshook__',\n", 263 | " '__truediv__',\n", 264 | " '__unicode__',\n", 265 | " '__weakref__',\n", 266 | " '__xor__',\n", 267 | " '_accessors',\n", 268 | " '_add_comparison_methods',\n", 269 | " '_add_logical_methods',\n", 270 | " '_add_logical_methods_disabled',\n", 271 | " '_add_numeric_methods',\n", 272 | " '_add_numeric_methods_add_sub_disabled',\n", 273 | " '_add_numeric_methods_binary',\n", 274 | " '_add_numeric_methods_disabled',\n", 275 | " '_add_numeric_methods_unary',\n", 276 | " '_assert_can_do_op',\n", 277 | " '_assert_can_do_setop',\n", 278 | " '_assert_take_fillable',\n", 279 | " '_attributes',\n", 280 | " '_can_hold_identifiers_and_holds_name',\n", 281 | " '_can_hold_na',\n", 282 | " '_can_reindex',\n", 283 | " '_cleanup',\n", 284 | " '_coerce_scalar_to_index',\n", 285 | " '_coerce_to_ndarray',\n", 286 | " '_comparables',\n", 287 | " '_concat',\n", 288 | " '_concat_same_dtype',\n", 289 | " '_constructor',\n", 290 | " '_convert_arr_indexer',\n", 291 | " '_convert_can_do_setop',\n", 292 | " '_convert_for_op',\n", 293 | " '_convert_index_indexer',\n", 294 | " '_convert_list_indexer',\n", 295 | " '_convert_listlike_indexer',\n", 296 | " '_convert_scalar_indexer',\n", 297 | " '_convert_slice_indexer',\n", 298 | " '_convert_tolerance',\n", 299 | " '_data',\n", 300 | " '_defer_to_indexing',\n", 301 | " '_deprecations',\n", 302 | " '_dir_additions',\n", 303 | " '_dir_deletions',\n", 304 | " '_engine',\n", 305 | " '_engine_type',\n", 306 | " '_evaluate_with_datetime_like',\n", 307 | " '_evaluate_with_timedelta_like',\n", 308 | " '_filter_indexer_tolerance',\n", 309 | " '_format_attrs',\n", 310 | " '_format_data',\n", 311 | " '_format_native_types',\n", 312 | " '_format_space',\n", 313 | " '_format_with_header',\n", 314 | " '_formatter_func',\n", 315 | " '_get_attributes_dict',\n", 316 | " '_get_fill_indexer',\n", 317 | " '_get_fill_indexer_searchsorted',\n", 318 | " '_get_grouper_for_level',\n", 319 | " '_get_level_number',\n", 320 | " '_get_level_values',\n", 321 | " '_get_loc_only_exact_matches',\n", 322 | " '_get_names',\n", 323 | " '_get_nearest_indexer',\n", 324 | " '_get_reconciled_name_object',\n", 325 | " '_get_string_slice',\n", 326 | " '_get_unique_index',\n", 327 | " '_has_complex_internals',\n", 328 | " '_id',\n", 329 | " '_infer_as_myclass',\n", 330 | " '_inner_indexer',\n", 331 | " '_invalid_indexer',\n", 332 | " '_is_homogeneous_type',\n", 333 | " '_is_memory_usage_qualified',\n", 334 | " '_is_numeric_dtype',\n", 335 | " '_is_strictly_monotonic_decreasing',\n", 336 | " '_is_strictly_monotonic_increasing',\n", 337 | " '_isnan',\n", 338 | " '_join_level',\n", 339 | " '_join_monotonic',\n", 340 | " '_join_multi',\n", 341 | " '_join_non_unique',\n", 342 | " '_join_precedence',\n", 343 | " '_left_indexer',\n", 344 | " '_left_indexer_unique',\n", 345 | " '_map_values',\n", 346 | " '_maybe_cast_indexer',\n", 347 | " '_maybe_cast_slice_bound',\n", 348 | " '_maybe_promote',\n", 349 | " '_maybe_update_attributes',\n", 350 | " '_mpl_repr',\n", 351 | " '_na_value',\n", 352 | " '_nan_idxs',\n", 353 | " '_ndarray_values',\n", 354 | " '_outer_indexer',\n", 355 | " '_reduce',\n", 356 | " '_reindex_non_unique',\n", 357 | " '_reset_cache',\n", 358 | " '_reset_identity',\n", 359 | " '_scalar_data_error',\n", 360 | " '_searchsorted_monotonic',\n", 361 | " '_set_names',\n", 362 | " '_shallow_copy',\n", 363 | " '_shallow_copy_with_infer',\n", 364 | " '_simple_new',\n", 365 | " '_sort_levels_monotonic',\n", 366 | " '_string_data_error',\n", 367 | " '_summary',\n", 368 | " '_to_safe_for_reshape',\n", 369 | " '_try_convert_to_int_index',\n", 370 | " '_typ',\n", 371 | " '_unpickle_compat',\n", 372 | " '_update_inplace',\n", 373 | " '_validate_for_numeric_binop',\n", 374 | " '_validate_for_numeric_unaryop',\n", 375 | " '_validate_index_level',\n", 376 | " '_validate_indexer',\n", 377 | " '_validate_names',\n", 378 | " '_validate_sort_keyword',\n", 379 | " '_values',\n", 380 | " '_wrap_joined_index',\n", 381 | " '_wrap_setop_result',\n", 382 | " 'all',\n", 383 | " 'any',\n", 384 | " 'append',\n", 385 | " 'argmax',\n", 386 | " 'argmin',\n", 387 | " 'argsort',\n", 388 | " 'array',\n", 389 | " 'asi8',\n", 390 | " 'asof',\n", 391 | " 'asof_locs',\n", 392 | " 'astype',\n", 393 | " 'contains',\n", 394 | " 'copy',\n", 395 | " 'delete',\n", 396 | " 'difference',\n", 397 | " 'drop',\n", 398 | " 'drop_duplicates',\n", 399 | " 'droplevel',\n", 400 | " 'dropna',\n", 401 | " 'dtype',\n", 402 | " 'dtype_str',\n", 403 | " 'duplicated',\n", 404 | " 'empty',\n", 405 | " 'equals',\n", 406 | " 'factorize',\n", 407 | " 'fillna',\n", 408 | " 'format',\n", 409 | " 'get_duplicates',\n", 410 | " 'get_indexer',\n", 411 | " 'get_indexer_for',\n", 412 | " 'get_indexer_non_unique',\n", 413 | " 'get_level_values',\n", 414 | " 'get_loc',\n", 415 | " 'get_slice_bound',\n", 416 | " 'get_value',\n", 417 | " 'get_values',\n", 418 | " 'groupby',\n", 419 | " 'has_duplicates',\n", 420 | " 'hasnans',\n", 421 | " 'holds_integer',\n", 422 | " 'identical',\n", 423 | " 'inferred_type',\n", 424 | " 'insert',\n", 425 | " 'intersection',\n", 426 | " 'is_',\n", 427 | " 'is_all_dates',\n", 428 | " 'is_boolean',\n", 429 | " 'is_categorical',\n", 430 | " 'is_floating',\n", 431 | " 'is_integer',\n", 432 | " 'is_interval',\n", 433 | " 'is_lexsorted_for_tuple',\n", 434 | " 'is_mixed',\n", 435 | " 'is_monotonic',\n", 436 | " 'is_monotonic_decreasing',\n", 437 | " 'is_monotonic_increasing',\n", 438 | " 'is_numeric',\n", 439 | " 'is_object',\n", 440 | " 'is_type_compatible',\n", 441 | " 'is_unique',\n", 442 | " 'isin',\n", 443 | " 'isna',\n", 444 | " 'isnull',\n", 445 | " 'item',\n", 446 | " 'join',\n", 447 | " 'map',\n", 448 | " 'max',\n", 449 | " 'memory_usage',\n", 450 | " 'min',\n", 451 | " 'name',\n", 452 | " 'names',\n", 453 | " 'nbytes',\n", 454 | " 'ndim',\n", 455 | " 'nlevels',\n", 456 | " 'notna',\n", 457 | " 'notnull',\n", 458 | " 'nunique',\n", 459 | " 'putmask',\n", 460 | " 'ravel',\n", 461 | " 'reindex',\n", 462 | " 'rename',\n", 463 | " 'repeat',\n", 464 | " 'searchsorted',\n", 465 | " 'set_names',\n", 466 | " 'set_value',\n", 467 | " 'shape',\n", 468 | " 'shift',\n", 469 | " 'size',\n", 470 | " 'slice_indexer',\n", 471 | " 'slice_locs',\n", 472 | " 'sort',\n", 473 | " 'sort_values',\n", 474 | " 'sortlevel',\n", 475 | " 'str',\n", 476 | " 'summary',\n", 477 | " 'symmetric_difference',\n", 478 | " 'take',\n", 479 | " 'to_flat_index',\n", 480 | " 'to_frame',\n", 481 | " 'to_list',\n", 482 | " 'to_native_types',\n", 483 | " 'to_numpy',\n", 484 | " 'to_series',\n", 485 | " 'transpose',\n", 486 | " 'union',\n", 487 | " 'unique',\n", 488 | " 'value_counts',\n", 489 | " 'values',\n", 490 | " 'view',\n", 491 | " 'where']" 492 | ] 493 | }, 494 | "execution_count": 5, 495 | "metadata": {}, 496 | "output_type": "execute_result" 497 | } 498 | ], 499 | "source": [ 500 | "## Features of Columns\n", 501 | "dir(df.columns)" 502 | ] 503 | }, 504 | { 505 | "cell_type": "code", 506 | "execution_count": 6, 507 | "metadata": {}, 508 | "outputs": [ 509 | { 510 | "data": { 511 | "text/plain": [ 512 | "array(['First Name', 'Last name', 'Age', 'SALARY', 'STREET Address1',\n", 513 | " 'STREET Address2', 'STREET Address3', 'email'], dtype=object)" 514 | ] 515 | }, 516 | "execution_count": 6, 517 | "metadata": {}, 518 | "output_type": "execute_result" 519 | } 520 | ], 521 | "source": [ 522 | "### Get The Columns As an Array\n", 523 | "df.columns.values" 524 | ] 525 | }, 526 | { 527 | "cell_type": "code", 528 | "execution_count": 7, 529 | "metadata": {}, 530 | "outputs": [ 531 | { 532 | "data": { 533 | "text/plain": [ 534 | "['First Name',\n", 535 | " 'Last name',\n", 536 | " 'Age',\n", 537 | " 'SALARY',\n", 538 | " 'STREET Address1',\n", 539 | " 'STREET Address2',\n", 540 | " 'STREET Address3',\n", 541 | " 'email']" 542 | ] 543 | }, 544 | "execution_count": 7, 545 | "metadata": {}, 546 | "output_type": "execute_result" 547 | } 548 | ], 549 | "source": [ 550 | "### Get The Columns As List\n", 551 | "df.columns.tolist()" 552 | ] 553 | }, 554 | { 555 | "cell_type": "code", 556 | "execution_count": 8, 557 | "metadata": {}, 558 | "outputs": [ 559 | { 560 | "data": { 561 | "text/plain": [ 562 | "Index(['First Name', 'Last name', 'Age', 'SALARY', 'STREET Address1',\n", 563 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 564 | " dtype='object')" 565 | ] 566 | }, 567 | "execution_count": 8, 568 | "metadata": {}, 569 | "output_type": "execute_result" 570 | } 571 | ], 572 | "source": [ 573 | "### To View Columns Names\n", 574 | "df.columns.view()" 575 | ] 576 | }, 577 | { 578 | "cell_type": "code", 579 | "execution_count": 9, 580 | "metadata": {}, 581 | "outputs": [ 582 | { 583 | "name": "stderr", 584 | "output_type": "stream", 585 | "text": [ 586 | "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: 'summary' is deprecated and will be removed in a future version.\n", 587 | " \n" 588 | ] 589 | }, 590 | { 591 | "data": { 592 | "text/plain": [ 593 | "'Index: 8 entries, First Name to email'" 594 | ] 595 | }, 596 | "execution_count": 9, 597 | "metadata": {}, 598 | "output_type": "execute_result" 599 | } 600 | ], 601 | "source": [ 602 | "### To View a Summary of the Column Names\n", 603 | "df.columns.summary()" 604 | ] 605 | }, 606 | { 607 | "cell_type": "code", 608 | "execution_count": 10, 609 | "metadata": {}, 610 | "outputs": [ 611 | { 612 | "data": { 613 | "text/plain": [ 614 | "First Name First Name\n", 615 | "Last name Last name\n", 616 | "Age Age\n", 617 | "SALARY SALARY\n", 618 | "STREET Address1 STREET Address1\n", 619 | "STREET Address2 STREET Address2\n", 620 | "STREET Address3 STREET Address3\n", 621 | "email email\n", 622 | "dtype: object" 623 | ] 624 | }, 625 | "execution_count": 10, 626 | "metadata": {}, 627 | "output_type": "execute_result" 628 | } 629 | ], 630 | "source": [ 631 | "# Convert the Column Names To Series/ DataFrame\n", 632 | "df.columns.to_series()" 633 | ] 634 | }, 635 | { 636 | "cell_type": "code", 637 | "execution_count": 11, 638 | "metadata": {}, 639 | "outputs": [ 640 | { 641 | "data": { 642 | "text/html": [ 643 | "
\n", 644 | "\n", 657 | "\n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | "
0
First NameFirst Name
Last nameLast name
AgeAge
SALARYSALARY
STREET Address1STREET Address1
STREET Address2STREET Address2
STREET Address3STREET Address3
emailemail
\n", 699 | "
" 700 | ], 701 | "text/plain": [ 702 | " 0\n", 703 | "First Name First Name\n", 704 | "Last name Last name\n", 705 | "Age Age\n", 706 | "SALARY SALARY\n", 707 | "STREET Address1 STREET Address1\n", 708 | "STREET Address2 STREET Address2\n", 709 | "STREET Address3 STREET Address3\n", 710 | "email email" 711 | ] 712 | }, 713 | "execution_count": 11, 714 | "metadata": {}, 715 | "output_type": "execute_result" 716 | } 717 | ], 718 | "source": [ 719 | "# Convert the Column Names To DataFrame\n", 720 | "df.columns.to_frame()" 721 | ] 722 | }, 723 | { 724 | "cell_type": "code", 725 | "execution_count": 12, 726 | "metadata": {}, 727 | "outputs": [ 728 | { 729 | "data": { 730 | "text/plain": [ 731 | "True" 732 | ] 733 | }, 734 | "execution_count": 12, 735 | "metadata": {}, 736 | "output_type": "execute_result" 737 | } 738 | ], 739 | "source": [ 740 | "# Check to see if column names contains a phrase\n", 741 | "df.columns.contains('First Name')" 742 | ] 743 | }, 744 | { 745 | "cell_type": "code", 746 | "execution_count": 13, 747 | "metadata": {}, 748 | "outputs": [ 749 | { 750 | "data": { 751 | "text/plain": [ 752 | "array([False, False, False, False, False, False, False, False])" 753 | ] 754 | }, 755 | "execution_count": 13, 756 | "metadata": {}, 757 | "output_type": "execute_result" 758 | } 759 | ], 760 | "source": [ 761 | "# Check to see if column names are duplicated\n", 762 | "df.columns.duplicated()" 763 | ] 764 | }, 765 | { 766 | "cell_type": "code", 767 | "execution_count": 14, 768 | "metadata": {}, 769 | "outputs": [ 770 | { 771 | "data": { 772 | "text/plain": [ 773 | "['__class__',\n", 774 | " '__delattr__',\n", 775 | " '__dict__',\n", 776 | " '__dir__',\n", 777 | " '__doc__',\n", 778 | " '__eq__',\n", 779 | " '__format__',\n", 780 | " '__frozen',\n", 781 | " '__ge__',\n", 782 | " '__getattribute__',\n", 783 | " '__getitem__',\n", 784 | " '__gt__',\n", 785 | " '__hash__',\n", 786 | " '__init__',\n", 787 | " '__init_subclass__',\n", 788 | " '__iter__',\n", 789 | " '__le__',\n", 790 | " '__lt__',\n", 791 | " '__module__',\n", 792 | " '__ne__',\n", 793 | " '__new__',\n", 794 | " '__reduce__',\n", 795 | " '__reduce_ex__',\n", 796 | " '__repr__',\n", 797 | " '__setattr__',\n", 798 | " '__sizeof__',\n", 799 | " '__str__',\n", 800 | " '__subclasshook__',\n", 801 | " '__weakref__',\n", 802 | " '_freeze',\n", 803 | " '_get_series_list',\n", 804 | " '_is_categorical',\n", 805 | " '_make_accessor',\n", 806 | " '_orig',\n", 807 | " '_parent',\n", 808 | " '_validate',\n", 809 | " '_wrap_result',\n", 810 | " 'capitalize',\n", 811 | " 'cat',\n", 812 | " 'center',\n", 813 | " 'contains',\n", 814 | " 'count',\n", 815 | " 'decode',\n", 816 | " 'encode',\n", 817 | " 'endswith',\n", 818 | " 'extract',\n", 819 | " 'extractall',\n", 820 | " 'find',\n", 821 | " 'findall',\n", 822 | " 'get',\n", 823 | " 'get_dummies',\n", 824 | " 'index',\n", 825 | " 'isalnum',\n", 826 | " 'isalpha',\n", 827 | " 'isdecimal',\n", 828 | " 'isdigit',\n", 829 | " 'islower',\n", 830 | " 'isnumeric',\n", 831 | " 'isspace',\n", 832 | " 'istitle',\n", 833 | " 'isupper',\n", 834 | " 'join',\n", 835 | " 'len',\n", 836 | " 'ljust',\n", 837 | " 'lower',\n", 838 | " 'lstrip',\n", 839 | " 'match',\n", 840 | " 'normalize',\n", 841 | " 'pad',\n", 842 | " 'partition',\n", 843 | " 'repeat',\n", 844 | " 'replace',\n", 845 | " 'rfind',\n", 846 | " 'rindex',\n", 847 | " 'rjust',\n", 848 | " 'rpartition',\n", 849 | " 'rsplit',\n", 850 | " 'rstrip',\n", 851 | " 'slice',\n", 852 | " 'slice_replace',\n", 853 | " 'split',\n", 854 | " 'startswith',\n", 855 | " 'strip',\n", 856 | " 'swapcase',\n", 857 | " 'title',\n", 858 | " 'translate',\n", 859 | " 'upper',\n", 860 | " 'wrap',\n", 861 | " 'zfill']" 862 | ] 863 | }, 864 | "execution_count": 14, 865 | "metadata": {}, 866 | "output_type": "execute_result" 867 | } 868 | ], 869 | "source": [ 870 | "### Attributes and Methods of Str\n", 871 | "dir(df.columns.str)" 872 | ] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": 15, 877 | "metadata": {}, 878 | "outputs": [ 879 | { 880 | "data": { 881 | "text/plain": [ 882 | "Index(['first name', 'last name', 'age', 'salary', 'street address1',\n", 883 | " 'street address2', 'street address3', 'email'],\n", 884 | " dtype='object')" 885 | ] 886 | }, 887 | "execution_count": 15, 888 | "metadata": {}, 889 | "output_type": "execute_result" 890 | } 891 | ], 892 | "source": [ 893 | "### Making Column Name Lower Case\n", 894 | "df.columns.str.lower()" 895 | ] 896 | }, 897 | { 898 | "cell_type": "code", 899 | "execution_count": 16, 900 | "metadata": {}, 901 | "outputs": [ 902 | { 903 | "data": { 904 | "text/plain": [ 905 | "Index(['FIRST NAME', 'LAST NAME', 'AGE', 'SALARY', 'STREET ADDRESS1',\n", 906 | " 'STREET ADDRESS2', 'STREET ADDRESS3', 'EMAIL'],\n", 907 | " dtype='object')" 908 | ] 909 | }, 910 | "execution_count": 16, 911 | "metadata": {}, 912 | "output_type": "execute_result" 913 | } 914 | ], 915 | "source": [ 916 | "### Making Column Name Upper Case\n", 917 | "df.columns.str.upper()" 918 | ] 919 | }, 920 | { 921 | "cell_type": "code", 922 | "execution_count": 17, 923 | "metadata": {}, 924 | "outputs": [ 925 | { 926 | "data": { 927 | "text/plain": [ 928 | "Index(['First Name', 'Last Name', 'Age', 'Salary', 'Street Address1',\n", 929 | " 'Street Address2', 'Street Address3', 'Email'],\n", 930 | " dtype='object')" 931 | ] 932 | }, 933 | "execution_count": 17, 934 | "metadata": {}, 935 | "output_type": "execute_result" 936 | } 937 | ], 938 | "source": [ 939 | "### Making Column Name Title Case\n", 940 | "df.columns.str.title()" 941 | ] 942 | }, 943 | { 944 | "cell_type": "code", 945 | "execution_count": 18, 946 | "metadata": {}, 947 | "outputs": [ 948 | { 949 | "data": { 950 | "text/plain": [ 951 | "Index(['First_Name', 'Last_name', 'Age', 'SALARY', 'STREET_Address1',\n", 952 | " 'STREET_Address2', 'STREET_Address3', 'email'],\n", 953 | " dtype='object')" 954 | ] 955 | }, 956 | "execution_count": 18, 957 | "metadata": {}, 958 | "output_type": "execute_result" 959 | } 960 | ], 961 | "source": [ 962 | "### Replacing Empty spaces with underscore\n", 963 | "df.columns.str.replace(' ','_')" 964 | ] 965 | }, 966 | { 967 | "cell_type": "code", 968 | "execution_count": 19, 969 | "metadata": {}, 970 | "outputs": [ 971 | { 972 | "data": { 973 | "text/html": [ 974 | "
\n", 975 | "\n", 988 | "\n", 989 | " \n", 990 | " \n", 991 | " \n", 992 | " \n", 993 | " \n", 994 | " \n", 995 | " \n", 996 | " \n", 997 | " \n", 998 | " \n", 999 | " \n", 1000 | " \n", 1001 | " \n", 1002 | " \n", 1003 | " \n", 1004 | " \n", 1005 | " \n", 1006 | " \n", 1007 | " \n", 1008 | " \n", 1009 | " \n", 1010 | " \n", 1011 | " \n", 1012 | " \n", 1013 | " \n", 1014 | " \n", 1015 | " \n", 1016 | " \n", 1017 | " \n", 1018 | " \n", 1019 | " \n", 1020 | " \n", 1021 | " \n", 1022 | " \n", 1023 | " \n", 1024 | " \n", 1025 | " \n", 1026 | " \n", 1027 | " \n", 1028 | " \n", 1029 | " \n", 1030 | " \n", 1031 | " \n", 1032 | " \n", 1033 | " \n", 1034 | " \n", 1035 | " \n", 1036 | " \n", 1037 | " \n", 1038 | " \n", 1039 | " \n", 1040 | " \n", 1041 | " \n", 1042 | " \n", 1043 | " \n", 1044 | " \n", 1045 | " \n", 1046 | " \n", 1047 | " \n", 1048 | " \n", 1049 | " \n", 1050 | " \n", 1051 | " \n", 1052 | " \n", 1053 | " \n", 1054 | " \n", 1055 | " \n", 1056 | " \n", 1057 | " \n", 1058 | " \n", 1059 | " \n", 1060 | " \n", 1061 | " \n", 1062 | " \n", 1063 | " \n", 1064 | " \n", 1065 | " \n", 1066 | " \n", 1067 | " \n", 1068 | " \n", 1069 | " \n", 1070 | " \n", 1071 | " \n", 1072 | " \n", 1073 | " \n", 1074 | " \n", 1075 | " \n", 1076 | " \n", 1077 | " \n", 1078 | " \n", 1079 | " \n", 1080 | " \n", 1081 | " \n", 1082 | " \n", 1083 | " \n", 1084 | " \n", 1085 | " \n", 1086 | " \n", 1087 | " \n", 1088 | " \n", 1089 | " \n", 1090 | " \n", 1091 | " \n", 1092 | " \n", 1093 | " \n", 1094 | " \n", 1095 | " \n", 1096 | " \n", 1097 | " \n", 1098 | " \n", 1099 | " \n", 1100 | " \n", 1101 | " \n", 1102 | " \n", 1103 | " \n", 1104 | " \n", 1105 | " \n", 1106 | " \n", 1107 | " \n", 1108 | " \n", 1109 | " \n", 1110 | " \n", 1111 | " \n", 1112 | " \n", 1113 | " \n", 1114 | " \n", 1115 | " \n", 1116 | " \n", 1117 | " \n", 1118 | " \n", 1119 | " \n", 1120 | " \n", 1121 | " \n", 1122 | " \n", 1123 | " \n", 1124 | " \n", 1125 | " \n", 1126 | " \n", 1127 | " \n", 1128 | " \n", 1129 | " \n", 1130 | " \n", 1131 | " \n", 1132 | " \n", 1133 | " \n", 1134 | " \n", 1135 | " \n", 1136 | " \n", 1137 | " \n", 1138 | " \n", 1139 | " \n", 1140 | " \n", 1141 | " \n", 1142 | " \n", 1143 | " \n", 1144 | " \n", 1145 | " \n", 1146 | " \n", 1147 | " \n", 1148 | " \n", 1149 | " \n", 1150 | " \n", 1151 | " \n", 1152 | " \n", 1153 | " \n", 1154 | " \n", 1155 | " \n", 1156 | " \n", 1157 | " \n", 1158 | " \n", 1159 | " \n", 1160 | " \n", 1161 | " \n", 1162 | " \n", 1163 | " \n", 1164 | " \n", 1165 | " \n", 1166 | " \n", 1167 | " \n", 1168 | " \n", 1169 | " \n", 1170 | " \n", 1171 | " \n", 1172 | " \n", 1173 | " \n", 1174 | " \n", 1175 | " \n", 1176 | " \n", 1177 | " \n", 1178 | " \n", 1179 | " \n", 1180 | " \n", 1181 | " \n", 1182 | " \n", 1183 | " \n", 1184 | " \n", 1185 | " \n", 1186 | " \n", 1187 | " \n", 1188 | " \n", 1189 | " \n", 1190 | " \n", 1191 | " \n", 1192 | " \n", 1193 | " \n", 1194 | " \n", 1195 | " \n", 1196 | " \n", 1197 | " \n", 1198 | " \n", 1199 | " \n", 1200 | " \n", 1201 | " \n", 1202 | " \n", 1203 | " \n", 1204 | " \n", 1205 | " \n", 1206 | " \n", 1207 | " \n", 1208 | " \n", 1209 | " \n", 1210 | " \n", 1211 | " \n", 1212 | " \n", 1213 | " \n", 1214 | " \n", 1215 | " \n", 1216 | " \n", 1217 | " \n", 1218 | " \n", 1219 | " \n", 1220 | " \n", 1221 | " \n", 1222 | " \n", 1223 | " \n", 1224 | " \n", 1225 | " \n", 1226 | " \n", 1227 | " \n", 1228 | " \n", 1229 | " \n", 1230 | " \n", 1231 | " \n", 1232 | " \n", 1233 | " \n", 1234 | " \n", 1235 | " \n", 1236 | " \n", 1237 | " \n", 1238 | " \n", 1239 | " \n", 1240 | " \n", 1241 | " \n", 1242 | " \n", 1243 | " \n", 1244 | " \n", 1245 | " \n", 1246 | " \n", 1247 | " \n", 1248 | " \n", 1249 | " \n", 1250 | " \n", 1251 | " \n", 1252 | " \n", 1253 | " \n", 1254 | " \n", 1255 | " \n", 1256 | " \n", 1257 | " \n", 1258 | " \n", 1259 | " \n", 1260 | " \n", 1261 | " \n", 1262 | " \n", 1263 | " \n", 1264 | " \n", 1265 | " \n", 1266 | " \n", 1267 | " \n", 1268 | " \n", 1269 | " \n", 1270 | " \n", 1271 | " \n", 1272 | " \n", 1273 | " \n", 1274 | " \n", 1275 | " \n", 1276 | " \n", 1277 | " \n", 1278 | " \n", 1279 | " \n", 1280 | " \n", 1281 | " \n", 1282 | " \n", 1283 | " \n", 1284 | " \n", 1285 | " \n", 1286 | " \n", 1287 | " \n", 1288 | " \n", 1289 | " \n", 1290 | " \n", 1291 | " \n", 1292 | " \n", 1293 | " \n", 1294 | " \n", 1295 | " \n", 1296 | " \n", 1297 | " \n", 1298 | " \n", 1299 | " \n", 1300 | " \n", 1301 | " \n", 1302 | " \n", 1303 | " \n", 1304 | " \n", 1305 | " \n", 1306 | " \n", 1307 | " \n", 1308 | " \n", 1309 | " \n", 1310 | " \n", 1311 | " \n", 1312 | " \n", 1313 | " \n", 1314 | " \n", 1315 | " \n", 1316 | " \n", 1317 | " \n", 1318 | " \n", 1319 | " \n", 1320 | " \n", 1321 | " \n", 1322 | " \n", 1323 | " \n", 1324 | " \n", 1325 | " \n", 1326 | " \n", 1327 | " \n", 1328 | " \n", 1329 | " \n", 1330 | " \n", 1331 | " \n", 1332 | " \n", 1333 | " \n", 1334 | " \n", 1335 | " \n", 1336 | " \n", 1337 | " \n", 1338 | " \n", 1339 | " \n", 1340 | " \n", 1341 | " \n", 1342 | " \n", 1343 | " \n", 1344 | " \n", 1345 | " \n", 1346 | " \n", 1347 | " \n", 1348 | " \n", 1349 | " \n", 1350 | " \n", 1351 | " \n", 1352 | " \n", 1353 | " \n", 1354 | " \n", 1355 | " \n", 1356 | " \n", 1357 | " \n", 1358 | " \n", 1359 | " \n", 1360 | " \n", 1361 | " \n", 1362 | " \n", 1363 | " \n", 1364 | " \n", 1365 | " \n", 1366 | " \n", 1367 | " \n", 1368 | " \n", 1369 | " \n", 1370 | " \n", 1371 | " \n", 1372 | " \n", 1373 | " \n", 1374 | " \n", 1375 | " \n", 1376 | " \n", 1377 | " \n", 1378 | " \n", 1379 | " \n", 1380 | " \n", 1381 | " \n", 1382 | " \n", 1383 | " \n", 1384 | " \n", 1385 | " \n", 1386 | " \n", 1387 | " \n", 1388 | " \n", 1389 | " \n", 1390 | " \n", 1391 | " \n", 1392 | " \n", 1393 | " \n", 1394 | " \n", 1395 | " \n", 1396 | " \n", 1397 | " \n", 1398 | " \n", 1399 | " \n", 1400 | " \n", 1401 | " \n", 1402 | " \n", 1403 | " \n", 1404 | " \n", 1405 | " \n", 1406 | " \n", 1407 | " \n", 1408 | " \n", 1409 | " \n", 1410 | " \n", 1411 | " \n", 1412 | " \n", 1413 | " \n", 1414 | " \n", 1415 | " \n", 1416 | " \n", 1417 | " \n", 1418 | " \n", 1419 | " \n", 1420 | " \n", 1421 | " \n", 1422 | " \n", 1423 | " \n", 1424 | " \n", 1425 | " \n", 1426 | " \n", 1427 | " \n", 1428 | " \n", 1429 | " \n", 1430 | " \n", 1431 | " \n", 1432 | " \n", 1433 | " \n", 1434 | " \n", 1435 | " \n", 1436 | " \n", 1437 | " \n", 1438 | " \n", 1439 | " \n", 1440 | " \n", 1441 | " \n", 1442 | " \n", 1443 | " \n", 1444 | " \n", 1445 | " \n", 1446 | " \n", 1447 | " \n", 1448 | " \n", 1449 | " \n", 1450 | " \n", 1451 | " \n", 1452 | " \n", 1453 | " \n", 1454 | " \n", 1455 | " \n", 1456 | " \n", 1457 | " \n", 1458 | " \n", 1459 | " \n", 1460 | " \n", 1461 | " \n", 1462 | " \n", 1463 | " \n", 1464 | " \n", 1465 | " \n", 1466 | " \n", 1467 | " \n", 1468 | " \n", 1469 | " \n", 1470 | " \n", 1471 | " \n", 1472 | " \n", 1473 | " \n", 1474 | " \n", 1475 | " \n", 1476 | " \n", 1477 | " \n", 1478 | " \n", 1479 | " \n", 1480 | " \n", 1481 | " \n", 1482 | " \n", 1483 | " \n", 1484 | " \n", 1485 | " \n", 1486 | " \n", 1487 | " \n", 1488 | " \n", 1489 | " \n", 1490 | " \n", 1491 | " \n", 1492 | " \n", 1493 | " \n", 1494 | " \n", 1495 | " \n", 1496 | " \n", 1497 | " \n", 1498 | " \n", 1499 | " \n", 1500 | " \n", 1501 | " \n", 1502 | " \n", 1503 | " \n", 1504 | " \n", 1505 | " \n", 1506 | " \n", 1507 | " \n", 1508 | " \n", 1509 | " \n", 1510 | " \n", 1511 | " \n", 1512 | " \n", 1513 | " \n", 1514 | " \n", 1515 | " \n", 1516 | " \n", 1517 | " \n", 1518 | " \n", 1519 | " \n", 1520 | " \n", 1521 | " \n", 1522 | " \n", 1523 | " \n", 1524 | " \n", 1525 | " \n", 1526 | " \n", 1527 | " \n", 1528 | " \n", 1529 | " \n", 1530 | " \n", 1531 | " \n", 1532 | " \n", 1533 | " \n", 1534 | " \n", 1535 | " \n", 1536 | " \n", 1537 | " \n", 1538 | " \n", 1539 | " \n", 1540 | " \n", 1541 | " \n", 1542 | " \n", 1543 | " \n", 1544 | " \n", 1545 | " \n", 1546 | " \n", 1547 | " \n", 1548 | " \n", 1549 | " \n", 1550 | " \n", 1551 | " \n", 1552 | " \n", 1553 | " \n", 1554 | " \n", 1555 | " \n", 1556 | " \n", 1557 | " \n", 1558 | " \n", 1559 | " \n", 1560 | " \n", 1561 | " \n", 1562 | " \n", 1563 | " \n", 1564 | " \n", 1565 | " \n", 1566 | " \n", 1567 | " \n", 1568 | " \n", 1569 | " \n", 1570 | " \n", 1571 | " \n", 1572 | " \n", 1573 | " \n", 1574 | " \n", 1575 | " \n", 1576 | " \n", 1577 | " \n", 1578 | " \n", 1579 | " \n", 1580 | " \n", 1581 | " \n", 1582 | " \n", 1583 | " \n", 1584 | " \n", 1585 | " \n", 1586 | " \n", 1587 | " \n", 1588 | " \n", 1589 | " \n", 1590 | " \n", 1591 | " \n", 1592 | " \n", 1593 | " \n", 1594 | " \n", 1595 | " \n", 1596 | " \n", 1597 | " \n", 1598 | " \n", 1599 | " \n", 1600 | " \n", 1601 | " \n", 1602 | " \n", 1603 | " \n", 1604 | " \n", 1605 | " \n", 1606 | " \n", 1607 | " \n", 1608 | " \n", 1609 | " \n", 1610 | " \n", 1611 | " \n", 1612 | " \n", 1613 | " \n", 1614 | " \n", 1615 | " \n", 1616 | " \n", 1617 | " \n", 1618 | " \n", 1619 | " \n", 1620 | " \n", 1621 | " \n", 1622 | " \n", 1623 | " \n", 1624 | " \n", 1625 | " \n", 1626 | " \n", 1627 | " \n", 1628 | " \n", 1629 | " \n", 1630 | " \n", 1631 | " \n", 1632 | " \n", 1633 | " \n", 1634 | " \n", 1635 | " \n", 1636 | " \n", 1637 | " \n", 1638 | " \n", 1639 | " \n", 1640 | " \n", 1641 | " \n", 1642 | " \n", 1643 | " \n", 1644 | " \n", 1645 | " \n", 1646 | " \n", 1647 | " \n", 1648 | " \n", 1649 | " \n", 1650 | " \n", 1651 | " \n", 1652 | " \n", 1653 | " \n", 1654 | " \n", 1655 | " \n", 1656 | " \n", 1657 | " \n", 1658 | " \n", 1659 | " \n", 1660 | " \n", 1661 | " \n", 1662 | " \n", 1663 | " \n", 1664 | " \n", 1665 | " \n", 1666 | " \n", 1667 | " \n", 1668 | " \n", 1669 | " \n", 1670 | " \n", 1671 | " \n", 1672 | " \n", 1673 | " \n", 1674 | " \n", 1675 | "
First NameLast nameDate of BirthSALARYSTREET Address1STREET Address2STREET Address3email
0JoelPadilla10/28/2019$92.32431-6530 Eu, Rd.364-2264 Augue Rd.P.O. Box 864, 3882 Orci Streeteu@nibh.com
1FritzTyler09/27/2019$83.91Ap #377-2267 Ac Av.979-2228 Vel Ave9865 Eu Av.est.ac.mattis@malesuadafringilla.net
2WingPhelps02/18/2019$17.15Ap #545-5786 Pulvinar AveAp #973-5781 Sagittis Avenue9959 Ut St.dolor@cubilia.net
3RyanRoss05/21/2019$45.97634-7858 Id Road907-8824 Fringilla Ave318-5271 In Aveinterdum.libero.dui@vitaeerat.com
4DrakeDay01/09/2020$84.38999-8221 Tempor, St.297-6939 Turpis. AveP.O. Box 638, 6932 Laoreet Rd.nulla.Integer.vulputate@liberoat.ca
5LucianWynn06/11/2019$0.54P.O. Box 650, 6721 Ut, Av.P.O. Box 963, 2210 Est St.1926 Posuere, Rd.molestie@nuncsitamet.com
6KasperVillarreal02/05/2019$47.696449 Ultrices Av.979-7052 Parturient Rd.P.O. Box 395, 3413 Tellus St.nec.orci@mi.com
7EmersonPratt03/01/2020$13.08405-6163 Mollis St.P.O. Box 963, 1079 Lorem, St.Ap #460-9797 Velit Roadsemper@Proinmi.net
8DustinFleming08/30/2019$61.609205 Maecenas AveP.O. Box 919, 9529 Donec AvenueP.O. Box 926, 8939 Fusce St.vulputate.velit.eu@lacusCras.co.uk
9AddisonJuarez07/02/2019$17.32322-2727 Lacinia Rd.P.O. Box 952, 7768 Sed RoadAp #644-6787 Pellentesque Rd.mollis.non@Quisquetinciduntpede.org
10XanthusColon05/29/2019$39.79653-2325 Aliquam Rd.P.O. Box 239, 1241 Diam Rd.9441 Duis AveSed.et@tristique.co.uk
11RaphaelLeonard12/01/2018$81.86Ap #915-7047 Aliquam AveAp #438-382 Ac Street7773 Odio. Avemollis.dui.in@scelerisque.co.uk
12OliverHartman03/15/2019$7.36Ap #840-7597 Risus. AveAp #424-8954 Erat. AvenueP.O. Box 859, 934 Eu Roadtellus.Suspendisse@gravida.ca
13DemetriusHurst07/08/2020$42.07P.O. Box 595, 3299 Metus St.4391 Parturient St.P.O. Box 401, 8266 Dictum StreetInteger.mollis.Integer@Phasellusvitaemauris.com
14KermitEnglish01/23/2019$92.75568-6252 Mus. Rd.P.O. Box 296, 1566 Cursus. Rd.3912 At Aveimperdiet.dictum.magna@lobortisClass.org
15ZahirTravis11/03/2019$27.64500-8214 Gravida. StreetP.O. Box 873, 7725 Pede, Rd.Ap #507-2527 Mollis. St.Quisque@semegestas.net
16JelaniCarroll11/21/2018$51.32817-746 Mattis. St.P.O. Box 762, 9695 Nisi. Rd.2847 Curabitur Roadleo.elementum@nislMaecenasmalesuada.ca
17ZacherySkinner08/24/2018$69.21750-2734 Eu St.1613 Interdum. Street5101 Nulla. Av.nulla.ante.iaculis@pede.co.uk
18JermaineOsborne08/07/2019$14.09Ap #695-2496 Enim AvenueAp #319-5081 Cras Ave914-3943 Pede RoadSed.eu.nibh@sapienmolestie.net
19CurranGay09/20/2018$17.96Ap #334-1963 Gravida StreetAp #859-6577 Accumsan Rd.934-3060 Enim. St.id.erat.Etiam@elitpede.ca
20GradySchroeder12/26/2018$79.333832 Morbi Rd.388-4974 Eleifend St.5403 Donec Rd.odio@eleifendCrassed.org
21MosesBarnett11/26/2018$30.79P.O. Box 337, 2447 Vestibulum, RoadAp #183-9990 Arcu. Rd.Ap #328-773 Magna. St.eleifend.nunc@milaciniamattis.org
22ToddRichmond06/26/2020$64.859020 Ac Rd.650-7083 Nec Rd.P.O. Box 658, 7970 Ac St.Duis.a.mi@nonummyFusce.net
23DanteSummers08/01/2019$90.14651-6550 Felis, Av.Ap #613-7918 Dui, Rd.9110 Vestibulum, St.dui.Fusce@Etiamvestibulummassa.co.uk
24ErasmusNieves07/01/2019$0.22626 Est. Avenue892-8304 Pellentesque Rd.8333 Pharetra. Av.Fusce.feugiat@ipsumdolor.ca
25ReedDuke11/25/2019$30.941903 Egestas St.6642 Fusce AveAp #788-4216 Ipsum RoadMorbi.neque@ultrices.com
26AbbotLove06/11/2020$29.24Ap #660-2020 Tincidunt Av.P.O. Box 486, 8215 Cras St.215-5661 Integer Avenuesed.tortor.Integer@quisarcuvel.ca
27GaryLowe10/12/2018$59.961524 Malesuada Av.9038 Consectetuer Rd.Ap #931-7278 Ridiculus Av.sit@Pellentesque.edu
28EatonWebster11/22/2019$0.418240 Vitae Rd.Ap #804-4838 Vitae St.334 Lacus. Rd.a@Integeraliquam.ca
29AllenOdom08/03/2019$20.60942 Duis RoadAp #266-6330 Conubia Ave1887 Eget Rd.Nunc@felis.com
...........................
70IraFrancis11/06/2018$43.03428-8034 Orci St.8708 Donec Rd.415 Neque Rd.blandit.Nam@Sed.net
71ClintonEmerson06/18/2019$45.08Ap #857-9020 Ipsum. StreetAp #469-4153 Luctus St.P.O. Box 748, 8147 Faucibus. St.euismod@seddui.org
72ThaddeusWorkman05/10/2019$6.86P.O. Box 925, 1576 Et St.P.O. Box 682, 3390 Leo. AveAp #962-5025 Ipsum Av.elit.erat.vitae@maurisipsumporta.com
73MarshallHarris02/04/2020$6.35P.O. Box 593, 7578 Nunc St.9197 Rutrum Avenue155-9329 Bibendum St.mollis@DonecegestasAliquam.com
74JinGould10/08/2019$48.66587-6660 Vel RoadP.O. Box 213, 7547 Pharetra, St.P.O. Box 886, 3113 Arcu. Roada.malesuada.id@Sed.ca
75UlricAlvarado09/25/2019$72.00217-9576 Libero. Street678-7906 Iaculis Rd.376-8891 Neque Avenueturpis.egestas@ultricesiaculisodio.com
76BrennanBerry08/03/2019$60.00Ap #892-1345 Tellus St.8733 Ligula St.Ap #248-2366 Nunc Rd.Nulla@enimdiamvel.com
77DriscollBurch04/10/2020$42.83Ap #345-6769 Lacus Avenue574-6143 Et St.105-5673 Vehicula Avesit@atvelit.ca
78DylanFigueroa02/10/2019$92.13164-9154 Vitae StreetAp #215-459 Proin Av.Ap #914-6812 Tempor Rd.nascetur.ridiculus.mus@vulputatenisisem.edu
79KnoxLuna10/29/2019$5.632523 In StreetAp #506-7675 Imperdiet Rd.Ap #321-3874 Vel Streetmagna.Phasellus.dolor@nectempus.org
80DeanDennis10/07/2019$58.39Ap #961-521 Ipsum Av.682-2710 Ac, Street680-3228 Tristique Av.eleifend.Cras@loremeumetus.co.uk
81ClarkAndrews06/03/2019$94.585076 Gravida. Rd.423-3585 Erat Street6084 Gravida St.nec@enim.co.uk
82JasperWilder08/23/2019$65.669586 Lorem, AvenueP.O. Box 237, 128 Integer Rd.6702 Libero Av.non.luctus@malesuadaiderat.ca
83AbbotRiley03/04/2020$30.38284-2591 Non Avenue5189 Nulla Street4878 Tempus Roadsollicitudin.orci@liberodui.net
84KeithMeyer08/28/2018$44.97P.O. Box 835, 3445 At, Avenue756-3192 Nec Road312-8233 Vehicula Avenuealiquet.magna.a@Suspendissecommodo.org
85KasimirChristensen09/09/2019$30.62P.O. Box 962, 2647 Purus. StreetP.O. Box 753, 1217 Ut, St.Ap #661-6774 Commodo Streetornare@risusodioauctor.edu
86KaneWoods11/20/2018$33.014968 A Ave451-2416 Pede, St.916-6138 Ultricies Av.morbi.tristique.senectus@euenimEtiam.co.uk
87HonoratoRuiz09/27/2019$29.15P.O. Box 838, 2148 Ut StreetP.O. Box 645, 3116 Neque. Road742-2419 Sem St.Nunc@natoquepenatibuset.net
88BorisRamirez04/11/2019$11.19P.O. Box 468, 5418 Velit Av.Ap #986-7943 Diam Rd.Ap #115-7485 Vivamus Rd.massa.non.ante@dui.edu
89BruceChurch01/03/2020$92.55280-3706 Adipiscing St.P.O. Box 396, 8141 Amet, AveAp #544-3906 Sed St.Nullam@Ut.ca
90DariusWarren07/18/2019$59.48255-1540 Dui. Rd.P.O. Box 999, 1286 Dolor. Ave5712 Pretium AveCum.sociis.natoque@liberoat.com
91AugustCleveland10/24/2019$98.53239-187 Venenatis Avenue710-6545 Diam. St.P.O. Box 656, 3450 Dui, Streetfaucibus.leo.in@arcu.net
92StuartMartin10/25/2019$25.95Ap #818-2389 Sed Rd.P.O. Box 922, 7474 Purus St.Ap #907-3803 Sed Roadsem.semper.erat@milaciniamattis.net
93HashimAndrews12/31/2018$56.50Ap #961-4075 Vitae, Road8722 Ornare Rd.9402 Nec Rd.libero.nec@SuspendisseduiFusce.org
94AdamLowery03/10/2019$56.22Ap #103-7642 Vivamus St.P.O. Box 146, 7542 Lacus. Rd.788-6809 Habitant Avealiquet@Proinvelarcu.edu
95VictorHobbs05/24/2019$54.564034 Vitae St.P.O. Box 930, 1683 Eu Rd.P.O. Box 181, 3360 Mus. Rd.ipsum@dictumaugue.com
96NeilBradford02/07/2020$74.521434 Aliquet, Street956-6627 Nunc Av.Ap #727-6109 Sapien. Av.sapien.Nunc@euodioPhasellus.net
97NobleConrad10/29/2019$43.99Ap #173-7049 Eget, St.Ap #620-2512 Ut Street8768 Aenean St.tellus.Nunc.lectus@ornare.org
98BrodyWhitaker08/09/2018$96.24Ap #371-9803 Aliquam Rd.8892 Euismod StreetAp #201-659 Libero. Streetnon.dapibus.rutrum@eumetus.co.uk
99AldenMccormick07/27/2019$2.66Ap #375-1139 Risus. Road7259 Duis Avenue955-4058 Maecenas St.ut.erat@aceleifend.com
\n", 1676 | "

100 rows × 8 columns

\n", 1677 | "
" 1678 | ], 1679 | "text/plain": [ 1680 | " First Name Last name Date of Birth SALARY \\\n", 1681 | "0 Joel Padilla 10/28/2019 $92.32 \n", 1682 | "1 Fritz Tyler 09/27/2019 $83.91 \n", 1683 | "2 Wing Phelps 02/18/2019 $17.15 \n", 1684 | "3 Ryan Ross 05/21/2019 $45.97 \n", 1685 | "4 Drake Day 01/09/2020 $84.38 \n", 1686 | "5 Lucian Wynn 06/11/2019 $0.54 \n", 1687 | "6 Kasper Villarreal 02/05/2019 $47.69 \n", 1688 | "7 Emerson Pratt 03/01/2020 $13.08 \n", 1689 | "8 Dustin Fleming 08/30/2019 $61.60 \n", 1690 | "9 Addison Juarez 07/02/2019 $17.32 \n", 1691 | "10 Xanthus Colon 05/29/2019 $39.79 \n", 1692 | "11 Raphael Leonard 12/01/2018 $81.86 \n", 1693 | "12 Oliver Hartman 03/15/2019 $7.36 \n", 1694 | "13 Demetrius Hurst 07/08/2020 $42.07 \n", 1695 | "14 Kermit English 01/23/2019 $92.75 \n", 1696 | "15 Zahir Travis 11/03/2019 $27.64 \n", 1697 | "16 Jelani Carroll 11/21/2018 $51.32 \n", 1698 | "17 Zachery Skinner 08/24/2018 $69.21 \n", 1699 | "18 Jermaine Osborne 08/07/2019 $14.09 \n", 1700 | "19 Curran Gay 09/20/2018 $17.96 \n", 1701 | "20 Grady Schroeder 12/26/2018 $79.33 \n", 1702 | "21 Moses Barnett 11/26/2018 $30.79 \n", 1703 | "22 Todd Richmond 06/26/2020 $64.85 \n", 1704 | "23 Dante Summers 08/01/2019 $90.14 \n", 1705 | "24 Erasmus Nieves 07/01/2019 $0.22 \n", 1706 | "25 Reed Duke 11/25/2019 $30.94 \n", 1707 | "26 Abbot Love 06/11/2020 $29.24 \n", 1708 | "27 Gary Lowe 10/12/2018 $59.96 \n", 1709 | "28 Eaton Webster 11/22/2019 $0.41 \n", 1710 | "29 Allen Odom 08/03/2019 $20.60 \n", 1711 | ".. ... ... ... ... \n", 1712 | "70 Ira Francis 11/06/2018 $43.03 \n", 1713 | "71 Clinton Emerson 06/18/2019 $45.08 \n", 1714 | "72 Thaddeus Workman 05/10/2019 $6.86 \n", 1715 | "73 Marshall Harris 02/04/2020 $6.35 \n", 1716 | "74 Jin Gould 10/08/2019 $48.66 \n", 1717 | "75 Ulric Alvarado 09/25/2019 $72.00 \n", 1718 | "76 Brennan Berry 08/03/2019 $60.00 \n", 1719 | "77 Driscoll Burch 04/10/2020 $42.83 \n", 1720 | "78 Dylan Figueroa 02/10/2019 $92.13 \n", 1721 | "79 Knox Luna 10/29/2019 $5.63 \n", 1722 | "80 Dean Dennis 10/07/2019 $58.39 \n", 1723 | "81 Clark Andrews 06/03/2019 $94.58 \n", 1724 | "82 Jasper Wilder 08/23/2019 $65.66 \n", 1725 | "83 Abbot Riley 03/04/2020 $30.38 \n", 1726 | "84 Keith Meyer 08/28/2018 $44.97 \n", 1727 | "85 Kasimir Christensen 09/09/2019 $30.62 \n", 1728 | "86 Kane Woods 11/20/2018 $33.01 \n", 1729 | "87 Honorato Ruiz 09/27/2019 $29.15 \n", 1730 | "88 Boris Ramirez 04/11/2019 $11.19 \n", 1731 | "89 Bruce Church 01/03/2020 $92.55 \n", 1732 | "90 Darius Warren 07/18/2019 $59.48 \n", 1733 | "91 August Cleveland 10/24/2019 $98.53 \n", 1734 | "92 Stuart Martin 10/25/2019 $25.95 \n", 1735 | "93 Hashim Andrews 12/31/2018 $56.50 \n", 1736 | "94 Adam Lowery 03/10/2019 $56.22 \n", 1737 | "95 Victor Hobbs 05/24/2019 $54.56 \n", 1738 | "96 Neil Bradford 02/07/2020 $74.52 \n", 1739 | "97 Noble Conrad 10/29/2019 $43.99 \n", 1740 | "98 Brody Whitaker 08/09/2018 $96.24 \n", 1741 | "99 Alden Mccormick 07/27/2019 $2.66 \n", 1742 | "\n", 1743 | " STREET Address1 STREET Address2 \\\n", 1744 | "0 431-6530 Eu, Rd. 364-2264 Augue Rd. \n", 1745 | "1 Ap #377-2267 Ac Av. 979-2228 Vel Ave \n", 1746 | "2 Ap #545-5786 Pulvinar Ave Ap #973-5781 Sagittis Avenue \n", 1747 | "3 634-7858 Id Road 907-8824 Fringilla Ave \n", 1748 | "4 999-8221 Tempor, St. 297-6939 Turpis. Ave \n", 1749 | "5 P.O. Box 650, 6721 Ut, Av. P.O. Box 963, 2210 Est St. \n", 1750 | "6 6449 Ultrices Av. 979-7052 Parturient Rd. \n", 1751 | "7 405-6163 Mollis St. P.O. Box 963, 1079 Lorem, St. \n", 1752 | "8 9205 Maecenas Ave P.O. Box 919, 9529 Donec Avenue \n", 1753 | "9 322-2727 Lacinia Rd. P.O. Box 952, 7768 Sed Road \n", 1754 | "10 653-2325 Aliquam Rd. P.O. Box 239, 1241 Diam Rd. \n", 1755 | "11 Ap #915-7047 Aliquam Ave Ap #438-382 Ac Street \n", 1756 | "12 Ap #840-7597 Risus. Ave Ap #424-8954 Erat. Avenue \n", 1757 | "13 P.O. Box 595, 3299 Metus St. 4391 Parturient St. \n", 1758 | "14 568-6252 Mus. Rd. P.O. Box 296, 1566 Cursus. Rd. \n", 1759 | "15 500-8214 Gravida. Street P.O. Box 873, 7725 Pede, Rd. \n", 1760 | "16 817-746 Mattis. St. P.O. Box 762, 9695 Nisi. Rd. \n", 1761 | "17 750-2734 Eu St. 1613 Interdum. Street \n", 1762 | "18 Ap #695-2496 Enim Avenue Ap #319-5081 Cras Ave \n", 1763 | "19 Ap #334-1963 Gravida Street Ap #859-6577 Accumsan Rd. \n", 1764 | "20 3832 Morbi Rd. 388-4974 Eleifend St. \n", 1765 | "21 P.O. Box 337, 2447 Vestibulum, Road Ap #183-9990 Arcu. Rd. \n", 1766 | "22 9020 Ac Rd. 650-7083 Nec Rd. \n", 1767 | "23 651-6550 Felis, Av. Ap #613-7918 Dui, Rd. \n", 1768 | "24 626 Est. Avenue 892-8304 Pellentesque Rd. \n", 1769 | "25 1903 Egestas St. 6642 Fusce Ave \n", 1770 | "26 Ap #660-2020 Tincidunt Av. P.O. Box 486, 8215 Cras St. \n", 1771 | "27 1524 Malesuada Av. 9038 Consectetuer Rd. \n", 1772 | "28 8240 Vitae Rd. Ap #804-4838 Vitae St. \n", 1773 | "29 942 Duis Road Ap #266-6330 Conubia Ave \n", 1774 | ".. ... ... \n", 1775 | "70 428-8034 Orci St. 8708 Donec Rd. \n", 1776 | "71 Ap #857-9020 Ipsum. Street Ap #469-4153 Luctus St. \n", 1777 | "72 P.O. Box 925, 1576 Et St. P.O. Box 682, 3390 Leo. Ave \n", 1778 | "73 P.O. Box 593, 7578 Nunc St. 9197 Rutrum Avenue \n", 1779 | "74 587-6660 Vel Road P.O. Box 213, 7547 Pharetra, St. \n", 1780 | "75 217-9576 Libero. Street 678-7906 Iaculis Rd. \n", 1781 | "76 Ap #892-1345 Tellus St. 8733 Ligula St. \n", 1782 | "77 Ap #345-6769 Lacus Avenue 574-6143 Et St. \n", 1783 | "78 164-9154 Vitae Street Ap #215-459 Proin Av. \n", 1784 | "79 2523 In Street Ap #506-7675 Imperdiet Rd. \n", 1785 | "80 Ap #961-521 Ipsum Av. 682-2710 Ac, Street \n", 1786 | "81 5076 Gravida. Rd. 423-3585 Erat Street \n", 1787 | "82 9586 Lorem, Avenue P.O. Box 237, 128 Integer Rd. \n", 1788 | "83 284-2591 Non Avenue 5189 Nulla Street \n", 1789 | "84 P.O. Box 835, 3445 At, Avenue 756-3192 Nec Road \n", 1790 | "85 P.O. Box 962, 2647 Purus. Street P.O. Box 753, 1217 Ut, St. \n", 1791 | "86 4968 A Ave 451-2416 Pede, St. \n", 1792 | "87 P.O. Box 838, 2148 Ut Street P.O. Box 645, 3116 Neque. Road \n", 1793 | "88 P.O. Box 468, 5418 Velit Av. Ap #986-7943 Diam Rd. \n", 1794 | "89 280-3706 Adipiscing St. P.O. Box 396, 8141 Amet, Ave \n", 1795 | "90 255-1540 Dui. Rd. P.O. Box 999, 1286 Dolor. Ave \n", 1796 | "91 239-187 Venenatis Avenue 710-6545 Diam. St. \n", 1797 | "92 Ap #818-2389 Sed Rd. P.O. Box 922, 7474 Purus St. \n", 1798 | "93 Ap #961-4075 Vitae, Road 8722 Ornare Rd. \n", 1799 | "94 Ap #103-7642 Vivamus St. P.O. Box 146, 7542 Lacus. Rd. \n", 1800 | "95 4034 Vitae St. P.O. Box 930, 1683 Eu Rd. \n", 1801 | "96 1434 Aliquet, Street 956-6627 Nunc Av. \n", 1802 | "97 Ap #173-7049 Eget, St. Ap #620-2512 Ut Street \n", 1803 | "98 Ap #371-9803 Aliquam Rd. 8892 Euismod Street \n", 1804 | "99 Ap #375-1139 Risus. Road 7259 Duis Avenue \n", 1805 | "\n", 1806 | " STREET Address3 \\\n", 1807 | "0 P.O. Box 864, 3882 Orci Street \n", 1808 | "1 9865 Eu Av. \n", 1809 | "2 9959 Ut St. \n", 1810 | "3 318-5271 In Ave \n", 1811 | "4 P.O. Box 638, 6932 Laoreet Rd. \n", 1812 | "5 1926 Posuere, Rd. \n", 1813 | "6 P.O. Box 395, 3413 Tellus St. \n", 1814 | "7 Ap #460-9797 Velit Road \n", 1815 | "8 P.O. Box 926, 8939 Fusce St. \n", 1816 | "9 Ap #644-6787 Pellentesque Rd. \n", 1817 | "10 9441 Duis Ave \n", 1818 | "11 7773 Odio. Ave \n", 1819 | "12 P.O. Box 859, 934 Eu Road \n", 1820 | "13 P.O. Box 401, 8266 Dictum Street \n", 1821 | "14 3912 At Ave \n", 1822 | "15 Ap #507-2527 Mollis. St. \n", 1823 | "16 2847 Curabitur Road \n", 1824 | "17 5101 Nulla. Av. \n", 1825 | "18 914-3943 Pede Road \n", 1826 | "19 934-3060 Enim. St. \n", 1827 | "20 5403 Donec Rd. \n", 1828 | "21 Ap #328-773 Magna. St. \n", 1829 | "22 P.O. Box 658, 7970 Ac St. \n", 1830 | "23 9110 Vestibulum, St. \n", 1831 | "24 8333 Pharetra. Av. \n", 1832 | "25 Ap #788-4216 Ipsum Road \n", 1833 | "26 215-5661 Integer Avenue \n", 1834 | "27 Ap #931-7278 Ridiculus Av. \n", 1835 | "28 334 Lacus. Rd. \n", 1836 | "29 1887 Eget Rd. \n", 1837 | ".. ... \n", 1838 | "70 415 Neque Rd. \n", 1839 | "71 P.O. Box 748, 8147 Faucibus. St. \n", 1840 | "72 Ap #962-5025 Ipsum Av. \n", 1841 | "73 155-9329 Bibendum St. \n", 1842 | "74 P.O. Box 886, 3113 Arcu. Road \n", 1843 | "75 376-8891 Neque Avenue \n", 1844 | "76 Ap #248-2366 Nunc Rd. \n", 1845 | "77 105-5673 Vehicula Ave \n", 1846 | "78 Ap #914-6812 Tempor Rd. \n", 1847 | "79 Ap #321-3874 Vel Street \n", 1848 | "80 680-3228 Tristique Av. \n", 1849 | "81 6084 Gravida St. \n", 1850 | "82 6702 Libero Av. \n", 1851 | "83 4878 Tempus Road \n", 1852 | "84 312-8233 Vehicula Avenue \n", 1853 | "85 Ap #661-6774 Commodo Street \n", 1854 | "86 916-6138 Ultricies Av. \n", 1855 | "87 742-2419 Sem St. \n", 1856 | "88 Ap #115-7485 Vivamus Rd. \n", 1857 | "89 Ap #544-3906 Sed St. \n", 1858 | "90 5712 Pretium Ave \n", 1859 | "91 P.O. Box 656, 3450 Dui, Street \n", 1860 | "92 Ap #907-3803 Sed Road \n", 1861 | "93 9402 Nec Rd. \n", 1862 | "94 788-6809 Habitant Ave \n", 1863 | "95 P.O. Box 181, 3360 Mus. Rd. \n", 1864 | "96 Ap #727-6109 Sapien. Av. \n", 1865 | "97 8768 Aenean St. \n", 1866 | "98 Ap #201-659 Libero. Street \n", 1867 | "99 955-4058 Maecenas St. \n", 1868 | "\n", 1869 | " email \n", 1870 | "0 eu@nibh.com \n", 1871 | "1 est.ac.mattis@malesuadafringilla.net \n", 1872 | "2 dolor@cubilia.net \n", 1873 | "3 interdum.libero.dui@vitaeerat.com \n", 1874 | "4 nulla.Integer.vulputate@liberoat.ca \n", 1875 | "5 molestie@nuncsitamet.com \n", 1876 | "6 nec.orci@mi.com \n", 1877 | "7 semper@Proinmi.net \n", 1878 | "8 vulputate.velit.eu@lacusCras.co.uk \n", 1879 | "9 mollis.non@Quisquetinciduntpede.org \n", 1880 | "10 Sed.et@tristique.co.uk \n", 1881 | "11 mollis.dui.in@scelerisque.co.uk \n", 1882 | "12 tellus.Suspendisse@gravida.ca \n", 1883 | "13 Integer.mollis.Integer@Phasellusvitaemauris.com \n", 1884 | "14 imperdiet.dictum.magna@lobortisClass.org \n", 1885 | "15 Quisque@semegestas.net \n", 1886 | "16 leo.elementum@nislMaecenasmalesuada.ca \n", 1887 | "17 nulla.ante.iaculis@pede.co.uk \n", 1888 | "18 Sed.eu.nibh@sapienmolestie.net \n", 1889 | "19 id.erat.Etiam@elitpede.ca \n", 1890 | "20 odio@eleifendCrassed.org \n", 1891 | "21 eleifend.nunc@milaciniamattis.org \n", 1892 | "22 Duis.a.mi@nonummyFusce.net \n", 1893 | "23 dui.Fusce@Etiamvestibulummassa.co.uk \n", 1894 | "24 Fusce.feugiat@ipsumdolor.ca \n", 1895 | "25 Morbi.neque@ultrices.com \n", 1896 | "26 sed.tortor.Integer@quisarcuvel.ca \n", 1897 | "27 sit@Pellentesque.edu \n", 1898 | "28 a@Integeraliquam.ca \n", 1899 | "29 Nunc@felis.com \n", 1900 | ".. ... \n", 1901 | "70 blandit.Nam@Sed.net \n", 1902 | "71 euismod@seddui.org \n", 1903 | "72 elit.erat.vitae@maurisipsumporta.com \n", 1904 | "73 mollis@DonecegestasAliquam.com \n", 1905 | "74 a.malesuada.id@Sed.ca \n", 1906 | "75 turpis.egestas@ultricesiaculisodio.com \n", 1907 | "76 Nulla@enimdiamvel.com \n", 1908 | "77 sit@atvelit.ca \n", 1909 | "78 nascetur.ridiculus.mus@vulputatenisisem.edu \n", 1910 | "79 magna.Phasellus.dolor@nectempus.org \n", 1911 | "80 eleifend.Cras@loremeumetus.co.uk \n", 1912 | "81 nec@enim.co.uk \n", 1913 | "82 non.luctus@malesuadaiderat.ca \n", 1914 | "83 sollicitudin.orci@liberodui.net \n", 1915 | "84 aliquet.magna.a@Suspendissecommodo.org \n", 1916 | "85 ornare@risusodioauctor.edu \n", 1917 | "86 morbi.tristique.senectus@euenimEtiam.co.uk \n", 1918 | "87 Nunc@natoquepenatibuset.net \n", 1919 | "88 massa.non.ante@dui.edu \n", 1920 | "89 Nullam@Ut.ca \n", 1921 | "90 Cum.sociis.natoque@liberoat.com \n", 1922 | "91 faucibus.leo.in@arcu.net \n", 1923 | "92 sem.semper.erat@milaciniamattis.net \n", 1924 | "93 libero.nec@SuspendisseduiFusce.org \n", 1925 | "94 aliquet@Proinvelarcu.edu \n", 1926 | "95 ipsum@dictumaugue.com \n", 1927 | "96 sapien.Nunc@euodioPhasellus.net \n", 1928 | "97 tellus.Nunc.lectus@ornare.org \n", 1929 | "98 non.dapibus.rutrum@eumetus.co.uk \n", 1930 | "99 ut.erat@aceleifend.com \n", 1931 | "\n", 1932 | "[100 rows x 8 columns]" 1933 | ] 1934 | }, 1935 | "execution_count": 19, 1936 | "metadata": {}, 1937 | "output_type": "execute_result" 1938 | } 1939 | ], 1940 | "source": [ 1941 | "### Renaming Column Name\n", 1942 | "df.rename(columns={'Age':'Date of Birth'})" 1943 | ] 1944 | }, 1945 | { 1946 | "cell_type": "code", 1947 | "execution_count": 20, 1948 | "metadata": {}, 1949 | "outputs": [], 1950 | "source": [ 1951 | "### Renaming Column Name /Inplace\n", 1952 | "df.rename(columns={'Age':'Date of Birth'},inplace=True)" 1953 | ] 1954 | }, 1955 | { 1956 | "cell_type": "code", 1957 | "execution_count": 21, 1958 | "metadata": {}, 1959 | "outputs": [ 1960 | { 1961 | "data": { 1962 | "text/plain": [ 1963 | "Index(['First Name', 'Last name', 'Date of Birth', 'SALARY', 'STREET Address1',\n", 1964 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 1965 | " dtype='object')" 1966 | ] 1967 | }, 1968 | "execution_count": 21, 1969 | "metadata": {}, 1970 | "output_type": "execute_result" 1971 | } 1972 | ], 1973 | "source": [ 1974 | "df.columns" 1975 | ] 1976 | }, 1977 | { 1978 | "cell_type": "code", 1979 | "execution_count": 22, 1980 | "metadata": {}, 1981 | "outputs": [ 1982 | { 1983 | "data": { 1984 | "text/plain": [ 1985 | "8" 1986 | ] 1987 | }, 1988 | "execution_count": 22, 1989 | "metadata": {}, 1990 | "output_type": "execute_result" 1991 | } 1992 | ], 1993 | "source": [ 1994 | "len(df.columns.values)\n" 1995 | ] 1996 | }, 1997 | { 1998 | "cell_type": "code", 1999 | "execution_count": 49, 2000 | "metadata": {}, 2001 | "outputs": [], 2002 | "source": [ 2003 | "# Renaming Column Names using select values\n", 2004 | "df.columns.values[7] = 'Email Address'" 2005 | ] 2006 | }, 2007 | { 2008 | "cell_type": "code", 2009 | "execution_count": 23, 2010 | "metadata": {}, 2011 | "outputs": [ 2012 | { 2013 | "data": { 2014 | "text/plain": [ 2015 | "Index(['First Name', 'Last name', 'Date of Birth', 'SALARY', 'STREET Address1',\n", 2016 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 2017 | " dtype='object')" 2018 | ] 2019 | }, 2020 | "execution_count": 23, 2021 | "metadata": {}, 2022 | "output_type": "execute_result" 2023 | } 2024 | ], 2025 | "source": [ 2026 | "df.columns" 2027 | ] 2028 | }, 2029 | { 2030 | "cell_type": "code", 2031 | "execution_count": 24, 2032 | "metadata": {}, 2033 | "outputs": [ 2034 | { 2035 | "data": { 2036 | "text/plain": [ 2037 | "Index(['First Name', 'Last name', 'Date of Birth', 'STREET Address1',\n", 2038 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 2039 | " dtype='object')" 2040 | ] 2041 | }, 2042 | "execution_count": 24, 2043 | "metadata": {}, 2044 | "output_type": "execute_result" 2045 | } 2046 | ], 2047 | "source": [ 2048 | "### Selecting All Columns Except One\n", 2049 | "df.columns[df.columns != 'SALARY']" 2050 | ] 2051 | }, 2052 | { 2053 | "cell_type": "code", 2054 | "execution_count": 25, 2055 | "metadata": {}, 2056 | "outputs": [ 2057 | { 2058 | "data": { 2059 | "text/plain": [ 2060 | "Index(['First Name', 'Last name', 'Date of Birth', 'STREET Address1',\n", 2061 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 2062 | " dtype='object')" 2063 | ] 2064 | }, 2065 | "execution_count": 25, 2066 | "metadata": {}, 2067 | "output_type": "execute_result" 2068 | } 2069 | ], 2070 | "source": [ 2071 | "### Selecting All Columns Except One\n", 2072 | "df.loc[:, df.columns != 'SALARY'].columns" 2073 | ] 2074 | }, 2075 | { 2076 | "cell_type": "code", 2077 | "execution_count": 26, 2078 | "metadata": {}, 2079 | "outputs": [ 2080 | { 2081 | "data": { 2082 | "text/plain": [ 2083 | "Index(['Date of Birth', 'First Name', 'Last name', 'STREET Address1',\n", 2084 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 2085 | " dtype='object')" 2086 | ] 2087 | }, 2088 | "execution_count": 26, 2089 | "metadata": {}, 2090 | "output_type": "execute_result" 2091 | } 2092 | ], 2093 | "source": [ 2094 | "# Select Column Names Except One Using Difference\n", 2095 | "df.columns.difference(['SALARY'])" 2096 | ] 2097 | }, 2098 | { 2099 | "cell_type": "code", 2100 | "execution_count": 27, 2101 | "metadata": {}, 2102 | "outputs": [ 2103 | { 2104 | "data": { 2105 | "text/plain": [ 2106 | "Index(['First Name', 'Last name', 'Date of Birth', 'STREET Address1',\n", 2107 | " 'STREET Address2', 'STREET Address3', 'email'],\n", 2108 | " dtype='object')" 2109 | ] 2110 | }, 2111 | "execution_count": 27, 2112 | "metadata": {}, 2113 | "output_type": "execute_result" 2114 | } 2115 | ], 2116 | "source": [ 2117 | "# Select Column Names Except One Using Negation of isin\n", 2118 | "df.loc[:,~df.columns.isin(['SALARY'])].columns" 2119 | ] 2120 | }, 2121 | { 2122 | "cell_type": "code", 2123 | "execution_count": 28, 2124 | "metadata": {}, 2125 | "outputs": [ 2126 | { 2127 | "data": { 2128 | "text/plain": [ 2129 | "Index(['STREET Address1', 'STREET Address2', 'STREET Address3'], dtype='object')" 2130 | ] 2131 | }, 2132 | "execution_count": 28, 2133 | "metadata": {}, 2134 | "output_type": "execute_result" 2135 | } 2136 | ], 2137 | "source": [ 2138 | "### Select Column Names that Begins with a Word or Character\n", 2139 | "df.filter(like='STREET').columns" 2140 | ] 2141 | }, 2142 | { 2143 | "cell_type": "code", 2144 | "execution_count": 29, 2145 | "metadata": {}, 2146 | "outputs": [ 2147 | { 2148 | "data": { 2149 | "text/plain": [ 2150 | "Index(['STREET Address1', 'STREET Address2', 'STREET Address3'], dtype='object')" 2151 | ] 2152 | }, 2153 | "execution_count": 29, 2154 | "metadata": {}, 2155 | "output_type": "execute_result" 2156 | } 2157 | ], 2158 | "source": [ 2159 | "### Select Column Names that Begins with a Word or Character\n", 2160 | "df.loc[:,df.columns.str.startswith('STREET')].columns" 2161 | ] 2162 | }, 2163 | { 2164 | "cell_type": "code", 2165 | "execution_count": 30, 2166 | "metadata": {}, 2167 | "outputs": [ 2168 | { 2169 | "data": { 2170 | "text/plain": [ 2171 | "Index(['First Name', 'Last name'], dtype='object')" 2172 | ] 2173 | }, 2174 | "execution_count": 30, 2175 | "metadata": {}, 2176 | "output_type": "execute_result" 2177 | } 2178 | ], 2179 | "source": [ 2180 | "### Select Column Names that ENDS with a Word or Character\n", 2181 | "df.loc[:,df.columns.str.endswith('ame')].columns" 2182 | ] 2183 | }, 2184 | { 2185 | "cell_type": "code", 2186 | "execution_count": 31, 2187 | "metadata": {}, 2188 | "outputs": [ 2189 | { 2190 | "data": { 2191 | "text/plain": [ 2192 | "Index(['First Name', 'Last name'], dtype='object')" 2193 | ] 2194 | }, 2195 | "execution_count": 31, 2196 | "metadata": {}, 2197 | "output_type": "execute_result" 2198 | } 2199 | ], 2200 | "source": [ 2201 | "### Select Column Names that ENDS with a Word or Character Using Filter and Regex name$\n", 2202 | "df.filter(regex='ame$',axis=1).columns" 2203 | ] 2204 | }, 2205 | { 2206 | "cell_type": "code", 2207 | "execution_count": 32, 2208 | "metadata": {}, 2209 | "outputs": [ 2210 | { 2211 | "data": { 2212 | "text/plain": [ 2213 | "array(['First Name', 'Last name', 'Date of Birth', 'SALARY'], dtype=object)" 2214 | ] 2215 | }, 2216 | "execution_count": 32, 2217 | "metadata": {}, 2218 | "output_type": "execute_result" 2219 | } 2220 | ], 2221 | "source": [ 2222 | "### Select A Group of Column Names\n", 2223 | "df.columns.values[0:4]" 2224 | ] 2225 | }, 2226 | { 2227 | "cell_type": "code", 2228 | "execution_count": 34, 2229 | "metadata": {}, 2230 | "outputs": [ 2231 | { 2232 | "data": { 2233 | "text/plain": [ 2234 | "Index(['First Name', 'Last name', 'Date of Birth', 'SALARY'], dtype='object')" 2235 | ] 2236 | }, 2237 | "execution_count": 34, 2238 | "metadata": {}, 2239 | "output_type": "execute_result" 2240 | } 2241 | ], 2242 | "source": [ 2243 | "### Select A Group of Column Names\n", 2244 | "df.columns[0:4]" 2245 | ] 2246 | }, 2247 | { 2248 | "cell_type": "code", 2249 | "execution_count": 35, 2250 | "metadata": {}, 2251 | "outputs": [], 2252 | "source": [ 2253 | "### Thanks For Watching\n", 2254 | "## Jesus Saves@JCharisTech \n", 2255 | "## Jesse E. Agbe(JCharis)\n", 2256 | "## J-Secur1ty" 2257 | ] 2258 | }, 2259 | { 2260 | "cell_type": "code", 2261 | "execution_count": null, 2262 | "metadata": {}, 2263 | "outputs": [], 2264 | "source": [] 2265 | } 2266 | ], 2267 | "metadata": { 2268 | "kernelspec": { 2269 | "display_name": "Python 3", 2270 | "language": "python", 2271 | "name": "python3" 2272 | }, 2273 | "language_info": { 2274 | "codemirror_mode": { 2275 | "name": "ipython", 2276 | "version": 3 2277 | }, 2278 | "file_extension": ".py", 2279 | "mimetype": "text/x-python", 2280 | "name": "python", 2281 | "nbconvert_exporter": "python", 2282 | "pygments_lexer": "ipython3", 2283 | "version": "3.6.8" 2284 | } 2285 | }, 2286 | "nbformat": 4, 2287 | "nbformat_minor": 2 2288 | } 2289 | -------------------------------------------------------------------------------- /Data Cleaning -Working with Column Names/raw_dataset.csv: -------------------------------------------------------------------------------- 1 | First Name,Last name,Age,SALARY,STREET Address1,STREET Address2,STREET Address3,email 2 | Joel,Padilla,10/28/2019,$92.32,"431-6530 Eu, Rd.",364-2264 Augue Rd.,"P.O. Box 864, 3882 Orci Street",eu@nibh.com 3 | Fritz,Tyler,09/27/2019,$83.91,Ap #377-2267 Ac Av.,979-2228 Vel Ave,9865 Eu Av.,est.ac.mattis@malesuadafringilla.net 4 | Wing,Phelps,02/18/2019,$17.15,Ap #545-5786 Pulvinar Ave,Ap #973-5781 Sagittis Avenue,9959 Ut St.,dolor@cubilia.net 5 | Ryan,Ross,05/21/2019,$45.97,634-7858 Id Road,907-8824 Fringilla Ave,318-5271 In Ave,interdum.libero.dui@vitaeerat.com 6 | Drake,Day,01/09/2020,$84.38,"999-8221 Tempor, St.",297-6939 Turpis. Ave,"P.O. Box 638, 6932 Laoreet Rd.",nulla.Integer.vulputate@liberoat.ca 7 | Lucian,Wynn,06/11/2019,$0.54,"P.O. Box 650, 6721 Ut, Av.","P.O. Box 963, 2210 Est St.","1926 Posuere, Rd.",molestie@nuncsitamet.com 8 | Kasper,Villarreal,02/05/2019,$47.69,6449 Ultrices Av.,979-7052 Parturient Rd.,"P.O. Box 395, 3413 Tellus St.",nec.orci@mi.com 9 | Emerson,Pratt,03/01/2020,$13.08,405-6163 Mollis St.,"P.O. Box 963, 1079 Lorem, St.",Ap #460-9797 Velit Road,semper@Proinmi.net 10 | Dustin,Fleming,08/30/2019,$61.60,9205 Maecenas Ave,"P.O. Box 919, 9529 Donec Avenue","P.O. Box 926, 8939 Fusce St.",vulputate.velit.eu@lacusCras.co.uk 11 | Addison,Juarez,07/02/2019,$17.32,322-2727 Lacinia Rd.,"P.O. Box 952, 7768 Sed Road",Ap #644-6787 Pellentesque Rd.,mollis.non@Quisquetinciduntpede.org 12 | Xanthus,Colon,05/29/2019,$39.79,653-2325 Aliquam Rd.,"P.O. Box 239, 1241 Diam Rd.",9441 Duis Ave,Sed.et@tristique.co.uk 13 | Raphael,Leonard,12/01/2018,$81.86,Ap #915-7047 Aliquam Ave,Ap #438-382 Ac Street,7773 Odio. Ave,mollis.dui.in@scelerisque.co.uk 14 | Oliver,Hartman,03/15/2019,$7.36,Ap #840-7597 Risus. Ave,Ap #424-8954 Erat. Avenue,"P.O. Box 859, 934 Eu Road",tellus.Suspendisse@gravida.ca 15 | Demetrius,Hurst,07/08/2020,$42.07,"P.O. Box 595, 3299 Metus St.",4391 Parturient St.,"P.O. Box 401, 8266 Dictum Street",Integer.mollis.Integer@Phasellusvitaemauris.com 16 | Kermit,English,01/23/2019,$92.75,568-6252 Mus. Rd.,"P.O. Box 296, 1566 Cursus. Rd.",3912 At Ave,imperdiet.dictum.magna@lobortisClass.org 17 | Zahir,Travis,11/03/2019,$27.64,500-8214 Gravida. Street,"P.O. Box 873, 7725 Pede, Rd.",Ap #507-2527 Mollis. St.,Quisque@semegestas.net 18 | Jelani,Carroll,11/21/2018,$51.32,817-746 Mattis. St.,"P.O. Box 762, 9695 Nisi. Rd.",2847 Curabitur Road,leo.elementum@nislMaecenasmalesuada.ca 19 | Zachery,Skinner,08/24/2018,$69.21,750-2734 Eu St.,1613 Interdum. Street,5101 Nulla. Av.,nulla.ante.iaculis@pede.co.uk 20 | Jermaine,Osborne,08/07/2019,$14.09,Ap #695-2496 Enim Avenue,Ap #319-5081 Cras Ave,914-3943 Pede Road,Sed.eu.nibh@sapienmolestie.net 21 | Curran,Gay,09/20/2018,$17.96,Ap #334-1963 Gravida Street,Ap #859-6577 Accumsan Rd.,934-3060 Enim. St.,id.erat.Etiam@elitpede.ca 22 | Grady,Schroeder,12/26/2018,$79.33,3832 Morbi Rd.,388-4974 Eleifend St.,5403 Donec Rd.,odio@eleifendCrassed.org 23 | Moses,Barnett,11/26/2018,$30.79,"P.O. Box 337, 2447 Vestibulum, Road",Ap #183-9990 Arcu. Rd.,Ap #328-773 Magna. St.,eleifend.nunc@milaciniamattis.org 24 | Todd,Richmond,06/26/2020,$64.85,9020 Ac Rd.,650-7083 Nec Rd.,"P.O. Box 658, 7970 Ac St.",Duis.a.mi@nonummyFusce.net 25 | Dante,Summers,08/01/2019,$90.14,"651-6550 Felis, Av.","Ap #613-7918 Dui, Rd.","9110 Vestibulum, St.",dui.Fusce@Etiamvestibulummassa.co.uk 26 | Erasmus,Nieves,07/01/2019,$0.22,626 Est. Avenue,892-8304 Pellentesque Rd.,8333 Pharetra. Av.,Fusce.feugiat@ipsumdolor.ca 27 | Reed,Duke,11/25/2019,$30.94,1903 Egestas St.,6642 Fusce Ave,Ap #788-4216 Ipsum Road,Morbi.neque@ultrices.com 28 | Abbot,Love,06/11/2020,$29.24,Ap #660-2020 Tincidunt Av.,"P.O. Box 486, 8215 Cras St.",215-5661 Integer Avenue,sed.tortor.Integer@quisarcuvel.ca 29 | Gary,Lowe,10/12/2018,$59.96,1524 Malesuada Av.,9038 Consectetuer Rd.,Ap #931-7278 Ridiculus Av.,sit@Pellentesque.edu 30 | Eaton,Webster,11/22/2019,$0.41,8240 Vitae Rd.,Ap #804-4838 Vitae St.,334 Lacus. Rd.,a@Integeraliquam.ca 31 | Allen,Odom,08/03/2019,$20.60,942 Duis Road,Ap #266-6330 Conubia Ave,1887 Eget Rd.,Nunc@felis.com 32 | Solomon,Gray,05/05/2020,$94.42,368-8417 Integer Avenue,"P.O. Box 627, 1783 Odio, St.",7777 Egestas Av.,egestas@necmauris.edu 33 | Zephania,Hamilton,06/06/2019,$73.25,"3424 Magna, St.",2775 Enim Av.,500-1222 Ligula. Rd.,lacus.Quisque@lacusCras.ca 34 | Wylie,Vasquez,02/08/2020,$15.15,"9710 Eu, St.",Ap #992-6350 Erat. Rd.,"P.O. Box 416, 7941 Enim Road",sit@cursusa.co.uk 35 | Reese,James,06/02/2019,$42.31,9474 Vitae Avenue,7403 Urna. Road,8494 Morbi Ave,Ut.semper@etarcu.com 36 | Elvis,Smith,12/03/2018,$70.00,"P.O. Box 904, 6663 Ut Rd.",899-8562 Etiam St.,620-1275 Maecenas Ave,Curae.Donec.tincidunt@nulla.ca 37 | Hu,Buckner,06/06/2020,$70.67,Ap #888-5585 Dui. Av.,"P.O. Box 643, 8599 Lobortis Rd.",4443 Mattis Rd.,Donec.nibh@erat.com 38 | Solomon,Willis,05/05/2020,$70.77,4681 Litora Avenue,Ap #922-373 Curabitur St.,Ap #907-5125 Eget St.,senectus.et.netus@laoreetlectusquis.org 39 | Seth,Jacobson,02/11/2019,$38.17,882-9064 Est Road,Ap #938-471 Scelerisque Street,Ap #883-5770 Ac Ave,natoque.penatibus@luctusvulputate.net 40 | Jackson,Hickman,02/21/2019,$3.43,Ap #907-3481 Nunc. Rd.,Ap #692-2366 Sed Av.,995-8006 Neque Av.,consectetuer@estmollis.net 41 | Murphy,Sloan,11/12/2018,$89.42,Ap #188-6469 Dictum. Ave,Ap #763-1429 Diam. Ave,"P.O. Box 786, 4820 Et, Road",Proin@egestas.net 42 | Elton,Delgado,05/11/2020,$88.40,3475 Velit. Ave,8687 Accumsan St.,8781 Feugiat Street,Nunc.pulvinar.arcu@nonmagna.org 43 | Arthur,Pratt,10/20/2018,$99.18,198-9173 Imperdiet Rd.,314-6677 Tempor Av.,Ap #871-3469 Fusce St.,pretium@vitae.edu 44 | Adam,Pickett,05/22/2020,$84.61,"P.O. Box 263, 4023 Blandit Ave","P.O. Box 696, 4081 Orci. Rd.","Ap #285-7032 Non, St.",pharetra.Quisque@ultricesDuisvolutpat.org 45 | Scott,Finch,02/02/2020,$44.31,"Ap #499-9982 Pede, Road",264-3753 Magna. Rd.,"671 Amet, Road",Donec.tempor@amifringilla.com 46 | Lane,Bender,02/05/2020,$84.02,"P.O. Box 232, 1543 Vehicula Avenue",9690 Vitae Road,"466-860 Et, Rd.",natoque@Aliquamadipiscinglobortis.net 47 | Connor,Kane,08/17/2018,$24.86,"P.O. Box 731, 428 Nisl St.","P.O. Box 409, 2528 Neque Rd.","P.O. Box 354, 5471 Posuere St.",dolor@consequat.org 48 | Ian,Conner,10/18/2019,$51.91,Ap #481-6655 Volutpat. Street,Ap #963-8049 Non Avenue,"P.O. Box 107, 5842 Nunc Ave",augue.Sed.molestie@acfermentumvel.co.uk 49 | Aquila,Moody,03/20/2020,$43.61,"P.O. Box 805, 1638 Duis Avenue",6726 Eu St.,"P.O. Box 562, 829 Maecenas Rd.",Donec.feugiat.metus@Nam.com 50 | Bruce,Lee,07/14/2020,$35.82,"9396 Ut, Avenue",Ap #429-401 Convallis Av.,629-426 Euismod Rd.,dictum.eu.placerat@leoCrasvehicula.ca 51 | Hop,Todd,08/07/2019,$9.73,Ap #274-8534 Sapien Road,5577 Nibh St.,Ap #400-7300 At Ave,placerat@nequeet.edu 52 | Caleb,Williamson,08/10/2019,$28.88,Ap #177-2906 Tellus Avenue,434-2958 Non Rd.,"P.O. Box 160, 3717 Dolor, Rd.",Vivamus.euismod.urna@Vestibulum.edu 53 | Martin,Hall,09/10/2018,$67.46,375-5583 Tempor Rd.,"P.O. Box 532, 2478 Erat Rd.","1623 Ultrices, St.",et.nunc.Quisque@arcuCurabiturut.edu 54 | Dillon,Carey,12/10/2018,$41.60,4432 Vitae Avenue,5735 Arcu Ave,179-8280 Sociis Road,non.leo.Vivamus@Praesenteudui.ca 55 | Xander,Battle,12/27/2018,$9.06,"P.O. Box 611, 6063 Molestie Ave",Ap #331-4458 Vulputate St.,"P.O. Box 512, 5869 Ligula. Rd.",facilisis@turpisnon.org 56 | Kermit,Mccarty,01/16/2019,$34.62,8134 Nisi Rd.,Ap #951-8366 Rutrum Rd.,"P.O. Box 948, 9386 Ligula. Rd.",ipsum.dolor@ametmassaQuisque.co.uk 57 | Lucian,Nguyen,10/23/2019,$98.80,"P.O. Box 561, 3550 Commodo Rd.",1188 Cursus Av.,Ap #771-2453 Donec Ave,Donec.sollicitudin@eget.ca 58 | Lucas,Combs,04/20/2020,$85.42,4745 Consectetuer Rd.,Ap #305-3135 Vivamus Ave,1896 Egestas Av.,vel.est@ultricesVivamus.ca 59 | Axel,Bowen,09/18/2019,$87.18,"332-2122 Vulputate, Av.",6831 Sem Road,"P.O. Box 617, 5705 Dictum. St.",ante@tincidunt.ca 60 | Melvin,Bentley,02/10/2019,$8.33,5599 Sed Street,"P.O. Box 227, 9657 Aliquet Rd.",967-1256 Primis Rd.,pellentesque@pede.co.uk 61 | Kuame,Kim,03/04/2020,$47.43,186-2332 Integer St.,"P.O. Box 842, 2600 Parturient Rd.","2291 Eget, Ave",at@diam.co.uk 62 | Fritz,Marquez,12/21/2018,$31.09,"P.O. Box 157, 1866 Viverra. Rd.",Ap #544-3677 Nullam Street,Ap #211-1060 Sit Rd.,per.conubia@egetipsumDonec.com 63 | Berk,Nunez,05/07/2019,$92.82,9834 Vitae Street,Ap #263-2841 Velit Avenue,Ap #466-2012 Ornare Av.,neque@nonummy.com 64 | Curran,Puckett,03/12/2020,$46.88,410-1572 Enim. Street,Ap #342-426 Curae; Street,178-5392 Eu Av.,vestibulum.neque.sed@facilisisvitae.edu 65 | Brent,Fischer,01/03/2019,$16.12,"253-6898 Non, Rd.","P.O. Box 864, 8018 Dictum Rd.",8382 Mauris St.,augue.eu@scelerisqueduiSuspendisse.com 66 | Forrest,Lambert,11/20/2018,$17.48,Ap #460-660 Aliquam Av.,Ap #937-713 Felis. Rd.,8369 Dictum Road,ipsum.cursus@accumsan.org 67 | Felix,Levy,10/02/2019,$86.48,6699 Molestie St.,"P.O. Box 784, 2127 Sit Rd.",Ap #974-8518 Sociosqu Rd.,eget@adipiscingligula.com 68 | Herrod,Kelly,07/27/2018,$37.48,290-9034 Dolor. St.,"P.O. Box 295, 108 Montes, Avenue","P.O. Box 355, 9154 Tristique Avenue",nunc.sit.amet@Suspendisseacmetus.ca 69 | Zahir,Conrad,12/01/2018,$58.59,4368 Arcu. Street,"P.O. Box 852, 3664 Est. Street",222-8313 Metus. Rd.,vel.sapien.imperdiet@enimEtiam.org 70 | Fulton,Gill,09/23/2018,$40.70,"850-996 Odio, St.",Ap #648-9574 Amet Rd.,Ap #710-6211 Donec Rd.,diam.lorem.auctor@velitinaliquet.net 71 | Jamal,Franks,06/21/2019,$70.32,Ap #948-7534 Ante. Rd.,5465 Ad St.,Ap #652-5804 Pede. Av.,ipsum.Donec.sollicitudin@orci.org 72 | Ira,Francis,11/06/2018,$43.03,428-8034 Orci St.,8708 Donec Rd.,415 Neque Rd.,blandit.Nam@Sed.net 73 | Clinton,Emerson,06/18/2019,$45.08,Ap #857-9020 Ipsum. Street,Ap #469-4153 Luctus St.,"P.O. Box 748, 8147 Faucibus. St.",euismod@seddui.org 74 | Thaddeus,Workman,05/10/2019,$6.86,"P.O. Box 925, 1576 Et St.","P.O. Box 682, 3390 Leo. Ave",Ap #962-5025 Ipsum Av.,elit.erat.vitae@maurisipsumporta.com 75 | Marshall,Harris,02/04/2020,$6.35,"P.O. Box 593, 7578 Nunc St.",9197 Rutrum Avenue,155-9329 Bibendum St.,mollis@DonecegestasAliquam.com 76 | Jin,Gould,10/08/2019,$48.66,587-6660 Vel Road,"P.O. Box 213, 7547 Pharetra, St.","P.O. Box 886, 3113 Arcu. Road",a.malesuada.id@Sed.ca 77 | Ulric,Alvarado,09/25/2019,$72.00,217-9576 Libero. Street,678-7906 Iaculis Rd.,376-8891 Neque Avenue,turpis.egestas@ultricesiaculisodio.com 78 | Brennan,Berry,08/03/2019,$60.00,Ap #892-1345 Tellus St.,8733 Ligula St.,Ap #248-2366 Nunc Rd.,Nulla@enimdiamvel.com 79 | Driscoll,Burch,04/10/2020,$42.83,Ap #345-6769 Lacus Avenue,574-6143 Et St.,105-5673 Vehicula Ave,sit@atvelit.ca 80 | Dylan,Figueroa,02/10/2019,$92.13,164-9154 Vitae Street,Ap #215-459 Proin Av.,Ap #914-6812 Tempor Rd.,nascetur.ridiculus.mus@vulputatenisisem.edu 81 | Knox,Luna,10/29/2019,$5.63,2523 In Street,Ap #506-7675 Imperdiet Rd.,Ap #321-3874 Vel Street,magna.Phasellus.dolor@nectempus.org 82 | Dean,Dennis,10/07/2019,$58.39,Ap #961-521 Ipsum Av.,"682-2710 Ac, Street",680-3228 Tristique Av.,eleifend.Cras@loremeumetus.co.uk 83 | Clark,Andrews,06/03/2019,$94.58,5076 Gravida. Rd.,423-3585 Erat Street,6084 Gravida St.,nec@enim.co.uk 84 | Jasper,Wilder,08/23/2019,$65.66,"9586 Lorem, Avenue","P.O. Box 237, 128 Integer Rd.",6702 Libero Av.,non.luctus@malesuadaiderat.ca 85 | Abbot,Riley,03/04/2020,$30.38,284-2591 Non Avenue,5189 Nulla Street,4878 Tempus Road,sollicitudin.orci@liberodui.net 86 | Keith,Meyer,08/28/2018,$44.97,"P.O. Box 835, 3445 At, Avenue",756-3192 Nec Road,312-8233 Vehicula Avenue,aliquet.magna.a@Suspendissecommodo.org 87 | Kasimir,Christensen,09/09/2019,$30.62,"P.O. Box 962, 2647 Purus. Street","P.O. Box 753, 1217 Ut, St.",Ap #661-6774 Commodo Street,ornare@risusodioauctor.edu 88 | Kane,Woods,11/20/2018,$33.01,4968 A Ave,"451-2416 Pede, St.",916-6138 Ultricies Av.,morbi.tristique.senectus@euenimEtiam.co.uk 89 | Honorato,Ruiz,09/27/2019,$29.15,"P.O. Box 838, 2148 Ut Street","P.O. Box 645, 3116 Neque. Road",742-2419 Sem St.,Nunc@natoquepenatibuset.net 90 | Boris,Ramirez,04/11/2019,$11.19,"P.O. Box 468, 5418 Velit Av.",Ap #986-7943 Diam Rd.,Ap #115-7485 Vivamus Rd.,massa.non.ante@dui.edu 91 | Bruce,Church,01/03/2020,$92.55,280-3706 Adipiscing St.,"P.O. Box 396, 8141 Amet, Ave",Ap #544-3906 Sed St.,Nullam@Ut.ca 92 | Darius,Warren,07/18/2019,$59.48,255-1540 Dui. Rd.,"P.O. Box 999, 1286 Dolor. Ave",5712 Pretium Ave,Cum.sociis.natoque@liberoat.com 93 | August,Cleveland,10/24/2019,$98.53,239-187 Venenatis Avenue,710-6545 Diam. St.,"P.O. Box 656, 3450 Dui, Street",faucibus.leo.in@arcu.net 94 | Stuart,Martin,10/25/2019,$25.95,Ap #818-2389 Sed Rd.,"P.O. Box 922, 7474 Purus St.",Ap #907-3803 Sed Road,sem.semper.erat@milaciniamattis.net 95 | Hashim,Andrews,12/31/2018,$56.50,"Ap #961-4075 Vitae, Road",8722 Ornare Rd.,9402 Nec Rd.,libero.nec@SuspendisseduiFusce.org 96 | Adam,Lowery,03/10/2019,$56.22,Ap #103-7642 Vivamus St.,"P.O. Box 146, 7542 Lacus. Rd.",788-6809 Habitant Ave,aliquet@Proinvelarcu.edu 97 | Victor,Hobbs,05/24/2019,$54.56,4034 Vitae St.,"P.O. Box 930, 1683 Eu Rd.","P.O. Box 181, 3360 Mus. Rd.",ipsum@dictumaugue.com 98 | Neil,Bradford,02/07/2020,$74.52,"1434 Aliquet, Street",956-6627 Nunc Av.,Ap #727-6109 Sapien. Av.,sapien.Nunc@euodioPhasellus.net 99 | Noble,Conrad,10/29/2019,$43.99,"Ap #173-7049 Eget, St.",Ap #620-2512 Ut Street,8768 Aenean St.,tellus.Nunc.lectus@ornare.org 100 | Brody,Whitaker,08/09/2018,$96.24,Ap #371-9803 Aliquam Rd.,8892 Euismod Street,Ap #201-659 Libero. Street,non.dapibus.rutrum@eumetus.co.uk 101 | Alden,Mccormick,07/27/2019,$2.66,Ap #375-1139 Risus. Road,7259 Duis Avenue,955-4058 Maecenas St.,ut.erat@aceleifend.com -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/julialogo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Julia - Reading Most Commonly Used File formats in DataScience with Julia/julialogo.png -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/myvalidjsonfile.json: -------------------------------------------------------------------------------- 1 | { 2 | "Employee":[ 3 | { 4 | "id":"1", 5 | "Name":"Peter", 6 | "Sal":"1000" 7 | }, 8 | { 9 | "id":"2", 10 | "Name":"Femi", 11 | "Sal":"2800" 12 | }, 13 | { 14 | "id":"3", 15 | "Name":"Emma", 16 | "Sal":"2400" 17 | }, 18 | { 19 | "id":"4", 20 | "Name":"Paul", 21 | "Sal":"2500" 22 | }, 23 | { 24 | "id":"5", 25 | "Name":"Mary", 26 | "Sal":"2400" 27 | } 28 | ] 29 | } -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.docx.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.docx.docx -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.h5 -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.hdf5 -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.txt: -------------------------------------------------------------------------------- 1 | hello world this is a text -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfile.zip -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfileexcel.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfileexcel.xlsx -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfiletab.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Julia - Reading Most Commonly Used File formats in DataScience with Julia/testfiletab.xlsx -------------------------------------------------------------------------------- /Julia - Reading Most Commonly Used File formats in DataScience with Julia/testhtml.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 21 | 22 | 23 | 24 |

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CompanyContactCountry
Alfreds FutterkisteMaria AndersGermany
Centro comercial MoctezumaFrancisco ChangMexico
Ernst HandelRoland MendelAustria
Island TradingHelen BennettUK
Laughing Bacchus WinecellarsYoshi TannamuriCanada
Magazzini Alimentari RiunitiGiovanni RovelliItaly
63 | 64 | 65 | 66 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data-Cleaning-Practical-Examples 2 | Data Cleaning In Python and Julia with Practical Examples 3 | -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplefile.docx.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Reading Most Commonly Used File Format in DataScience with Python/examplefile.docx.docx -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplefile.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Reading Most Commonly Used File Format in DataScience with Python/examplefile.h5 -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplefile.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Reading Most Commonly Used File Format in DataScience with Python/examplefile.hdf5 -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplefile.txt: -------------------------------------------------------------------------------- 1 | hello world this is a text -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplefile.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Reading Most Commonly Used File Format in DataScience with Python/examplefile.zip -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplefileexcel.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Reading Most Commonly Used File Format in DataScience with Python/examplefileexcel.xlsx -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplefiletab.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Reading Most Commonly Used File Format in DataScience with Python/examplefiletab.xlsx -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/examplehtml.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 21 | 22 | 23 | 24 |

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CompanyContactCountry
Alfreds FutterkisteMaria AndersGermany
Centro comercial MoctezumaFrancisco ChangMexico
Ernst HandelRoland MendelAustria
Island TradingHelen BennettUK
Laughing Bacchus WinecellarsYoshi TannamuriCanada
Magazzini Alimentari RiunitiGiovanni RovelliItaly
63 | 64 | 65 | 66 | -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/myexample.json: -------------------------------------------------------------------------------- 1 | { 2 | "Employee": [ 3 | 4 | { 5 | 6 | "id":"1", 7 | 8 | "Name": "Peter", 9 | 10 | "Sal": "1000", 11 | 12 | }, 13 | { 14 | 15 | "id":"2", 16 | 17 | "Name": "Femi", 18 | 19 | "Sal": "2800", 20 | 21 | }, 22 | { 23 | 24 | "id":"3", 25 | 26 | "Name": "Emma", 27 | 28 | "Sal": "2400", 29 | 30 | }, 31 | { 32 | 33 | "id":"4", 34 | 35 | "Name": "Paul", 36 | 37 | "Sal": "2500", 38 | 39 | }, 40 | { 41 | 42 | "id":"5", 43 | 44 | "Name": "Mary", 45 | 46 | "Sal": "2400", 47 | 48 | } 49 | 50 | ] 51 | 52 | } -------------------------------------------------------------------------------- /Reading Most Commonly Used File Format in DataScience with Python/myvalidjsonfile.json: 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https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/Reading Most Commonly Used File Format in DataScience with Python/sampleh5files/my97.h5 -------------------------------------------------------------------------------- /raw_data_unmodified.csv: -------------------------------------------------------------------------------- 1 | movie_title,num_critic_for_reviews,duration,DIRECTOR_facebook_likes,actor_3_facebook_likes,ACTOR_1_facebook_likes,gross,num_voted_users,Cast_Total_facebook_likes,facenumber_in_poster,num_user_for_reviews,budget,title_year,ACTOR_2_facebook_likes,imdb_score,title_year 2 | Avatar?ÿ,723,178,10,855,1000,760505847,886204,4834,,3054,237000000,2009,936,7.9,2009 3 | Pirates of the Caribbean: At World's End?ÿ,302,,563,1000,40000,309404152,471220,48350,,1238,300000000,2007,5000,7.1, 4 | Spectre?ÿ,602,148,20,161,11000,200074175,275868,11700,1,994,245000000,2015,393,6.8,2015 5 | The Dark Knight Rises?ÿ,813,,22000,23000,27000,448130642,1144337,106759,,2701,250000000,2012,23000,8.5, 6 | John Carter?ÿ,462,132,"""475""",530,640,73058679,212204,1873,1,738,263700000,2012,632,6.6, 7 | Spider-Man 3?ÿ,392,156,23,4000,24000,336530303,383056,46055,,1902,258000000,2007,11000,6.2,2007 8 | Tangled?ÿ,324,,15,284,799,200807262,294810,,1,387,260000000,2010,553,7.8, 9 | Avengers: Age of Ultron?ÿ,635,141,10,19000,26000,458991599,462669,92000,4,1117,250000000,2015,21000,7.5, 10 | Avengers: Age of Ultron?ÿ,635,141,10,19000,26000,458991599,462669,92000,4,1117,250000000,2015,21000,7.5,2015 11 | Harry Potter and the Half-Blood Prince?ÿ,375,153,282,10000,25000,301956980,321795,58753,3,973,250000000,2009,11000,7.5, 12 | Batman v Superman: Dawn of Justice?ÿ,673,183,,2000,15000,330249062,,24450,,3018,250000000,2016,,6.9,2016 13 | Superman Returns?ÿ,434,169,,903,18000,200069408,240396,,2,2367,209000000,2006,10000,6.1, 14 | Quantum of Solace?ÿ,403,106,395,393,451,168368427,330784,2023,1,1243,200000000,2008,412,6.7,2008 15 | Pirates of the Caribbean: Dead Man's Chest?ÿ,313,151,563,1000,40000,423032628,522040,48486,2,1832,225000000,2006,5000,7.3,2008 16 | -------------------------------------------------------------------------------- /unclean_data.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/unclean_data.csv -------------------------------------------------------------------------------- /unclean_data1.csv: -------------------------------------------------------------------------------- 1 | movie_title,num_critic_for_reviews,duration,DIRECTOR_facebook_likes,actor_3_facebook_likes,ACTOR_1_facebook_likes,gross,num_voted_users,Cast_Total_facebook_likes,facenumber_in_poster,num_user_for_reviews,budget,title_year,ACTOR_2_facebook_likes,imdb_score,title_year 2 | Avatar?ÿ,723,178,10,855,1000,760505847,886204,4834,,3054,237000000,2009,936,7.9,2009 3 | Pirates of the Caribbean: At World's End?ÿ,302,,563,1000,40000,309404152,471220,48350,,1238,300000000,2007,5000,7.1, 4 | Spectre?ÿ,602,148,20,161,11000,200074175,275868,11700,1,994,245000000,2015,393,6.8,2015 5 | The Dark Knight Rises?ÿ,813,,22000,23000,27000,448130642,1144337,106759,,2701,250000000,2012,23000,8.5, 6 | John Carter?ÿ,462,132,"""475""",530,640,73058679,212204,1873,1,738,263700000,2012,632,6.6, 7 | Spider-Man 3?ÿ,392,156,23,4000,24000,336530303,383056,46055,,1902,258000000,2007,11000,6.2,2007 8 | Tangled?ÿ,324,,15,284,799,200807262,294810,,1,387,260000000,2010,553,7.8, 9 | Avengers: Age of Ultron?ÿ,635,141,10,19000,26000,458991599,462669,92000,4,1117,250000000,2015,21000,7.5, 10 | Avengers: Age of Ultron?ÿ,635,141,10,19000,26000,458991599,462669,92000,4,1117,250000000,2015,21000,7.5,2015 11 | Harry Potter and the Half-Blood Prince?ÿ,375,153,282,10000,25000,301956980,321795,58753,3,973,250000000,2009,11000,7.5, 12 | Batman v Superman: Dawn of Justice?ÿ,673,183,,2000,15000,330249062,,24450,,3018,250000000,2016,,6.9,2016 13 | Superman Returns?ÿ,434,169,,903,18000,200069408,240396,,2,2367,209000000,2006,10000,6.1, 14 | Quantum of Solace?ÿ,403,106,395,393,451,168368427,330784,2023,1,1243,200000000,2008,412,6.7,2008 15 | Pirates of the Caribbean: Dead Man's Chest?ÿ,313,151,563,1000,40000,423032628,522040,48486,2,1832,225000000,2006,5000,7.3,2008 16 | -------------------------------------------------------------------------------- /unclean_data_unmodified.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jcharis/Data-Cleaning-Practical-Examples/91a8c6146df89593698257f68c9832bc8a9fb20f/unclean_data_unmodified.csv --------------------------------------------------------------------------------