Udemy The Ultimate Pandas Bootcamp Advanced Python Data Analysis

[0dayddl]

14.51 GB | 00:14:12 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 - Course Structure (21.94 MB)
2 - Pandas Is Not Single (27.23 MB)
3 - Anaconda (31.44 MB)
4 - Jupyter Notebooks (68.05 MB)
5 - Cloud vs Local (39.07 MB)
6 - Hello Python (48.25 MB)
7 - NumPy (70.33 MB)
8 - all-notebooks (1005.95 KB)
8 - slides (2.53 MB)
230 - Section Intro (37.9 MB)
231 - The Python datetime Module (59.26 MB)
232 - Parsing Dates From Text (78.84 MB)
233 - Even Better dateutil (36.99 MB)
234 - From Datetime To String (33.34 MB)
235 - Performant Datetimes With Numpy (54.01 MB)
236 - The Pandas Timestamp (36.04 MB)
237 - Our Dataset Brent Prices (43.41 MB)
238 - Date Parsing And DatetimeIndex (37.4 MB)
239 - A Cool Shorcut readcsv With parsedates (27.34 MB)
240 - Indexing Dates (39.65 MB)
241 - Skill Challenge (5.66 MB)
242 - Solution (25.39 MB)
243 - DateTimeIndex Attribute Accessors (58.09 MB)
244 - Creating Date Ranges (57.13 MB)
245 - Shifting Dates With pdDateOffset (54.45 MB)
246 - BONUS Timedeltas And Absolute Time (43.55 MB)
247 - Resampling Timeseries (55.46 MB)
248 - Upsampling And Interpolation (74.86 MB)
249 - What About asfreq (54.17 MB)
250 - BONUS Rolling Windows (63.86 MB)
251 - Skill Challenge (6.85 MB)
252 - Solution (33.9 MB)
253 - Handling-Time-And-Date ipynb (104.69 KB)
254 - Section Intro (23.63 MB)
255 - Our Data Boston Marathon Runners (36.02 MB)
256 - String Methods In Python (42.33 MB)
257 - Vectorized String Operations In Pandas (28.46 MB)
258 - Case Operations (20.99 MB)
259 - Finding Characters And Words (37.41 MB)
260 - Strips And Whitespace (48.04 MB)
261 - String Splitting And Concatenation (70.25 MB)
262 - More Split Parameters (61.45 MB)
263 - Skill Challenge (4.62 MB)
264 - Solution (34.24 MB)
265 - Slicing Substrings (36.44 MB)
266 - Masking With String Methods (56.74 MB)
267 - BONUS Parsing Indicators With getdummies (102.94 MB)
268 - Text Replacement (64.46 MB)
269 - Introduction To Regular Expressions (117.87 MB)
270 - More Regex Concepts (103.1 MB)
271 - How To Approach Regex (99.04 MB)
272 - Is This A Valid Email (121.91 MB)
273 - BONUS Whats The Point Of recompile (29.74 MB)
274 - Pandas str contains split And replace With Regex (117.94 MB)
275 - Skill Challenge (7.92 MB)
276 - Solution (111.36 MB)
277 - Regex-And-Text-Manipulation ipynb (29.78 KB)
278 - Section Intro (5.18 MB)
279 - The Art Of Data Visualization (19.58 MB)
280 - The Preliminaries Of matplotlib (93.68 MB)
281 - Line Graphs (80.87 MB)
282 - Bar Charts (72.37 MB)
283 - Pie Plots (82.42 MB)
284 - Histograms (64.56 MB)
285 - Scatter Plots (95.52 MB)
286 - Other Visualization Options (103.29 MB)
287 - BONUS Data Ink And Chartjunk (48.61 MB)
288 - Skill Challenge (11.45 MB)
289 - Solution (63.24 MB)
290 - Visualizing-Data ipynb (500.75 KB)
291 - Section Intro (2.73 MB)
292 - Reading JSON (22.65 MB)
293 - Reading HTML (146.26 MB)
294 - Reading Excel (83.37 MB)
295 - Creating Output The to Family Of Methods (111.5 MB)
296 - BONUS Introduction To Pickling (46.94 MB)
297 - Pickles In Pandas (33.69 MB)
297 - portfolio (1.42 KB)
298 - The Many Other Formats (42.11 MB)
299 - Skill Challenge (17.82 MB)
300 - Solution (70.21 MB)
301 - Data-Formats-And-I-O ipynb (23.64 KB)
302 - Section Intro (13.18 MB)
303 - Data Types (14.59 MB)
304 - Variables (57.62 MB)
305 - Arithmetic And Augmented Assignment Operators (38.65 MB)
306 - Ints And Floats (64.15 MB)
307 - Booleans And Comparison Operators (31.27 MB)
308 - Strings (46.23 MB)
309 - Methods (35.7 MB)
310 - Containers I Lists (43.62 MB)
311 - Lists vs Strings (39.01 MB)
312 - List Methods And Functions (47.21 MB)
313 - Containers II Tuples (29.17 MB)
314 - Containers III Sets (77.36 MB)
315 - Containers IV Dictionaries (33.66 MB)
316 - Dictionary Keys And Values (53.55 MB)
317 - Membership Operators (28.06 MB)
318 - Controlling Flow if else And elif (62.63 MB)
319 - Truth Value Of Nonbooleans (23.54 MB)
320 - For Loops (30.21 MB)
321 - The range Immutable Sequence (35.75 MB)
322 - While Loops (43.94 MB)
323 - Break And Continue (28.72 MB)
324 - Zipping Iterables (25.63 MB)
325 - List Comprehensions (46.66 MB)
326 - Defining Functions (86.94 MB)
327 - Function Arguments Positional vs Keyword (46.54 MB)
328 - Lambdas (33.95 MB)
329 - Importing Modules (50.62 MB)
330 - Appendix-A-Rapid-Fire-Python-Fundamentals ipynb (25.62 KB)
331 - Installing Anaconda And Python Windows (101.47 MB)
332 - Installing Anaconda And Python Mac (26.22 MB)
333 - Installing Anaconda And Python Linux (38.36 MB)
10 - What Is A Series (17.06 MB)
11 - Parameters vs Arguments (10.75 MB)
12 - Whats In The Data (29.64 MB)
13 - The dtype Attribute (9.1 MB)
14 - BONUS What Is dtypeo Really (14.34 MB)
15 - Index And RangeIndex (50.1 MB)
16 - Series And Index Names (28.16 MB)
17 - Skill Challenge (11.85 MB)
18 - Solution (36.39 MB)
19 - Another Solution (16.64 MB)
20 - The head And tail Methods (33.85 MB)
21 - Extracting By Index Position (42.03 MB)
22 - Accessing Elements By Label (40.11 MB)
23 - BONUS The addprefix And addsuffix Methods (24.66 MB)
24 - Using Dot Notation (19.18 MB)
25 - Boolean Masks And The loc Indexer (42.88 MB)
26 - Extracting By Position With iloc (16.55 MB)
27 - BONUS Using Callables With loc And iloc (53.63 MB)
28 - Selecting With get (47.11 MB)
29 - Selection Recap (40.98 MB)
30 - Skill Challenge (9.68 MB)
31 - Solution (34.84 MB)
32 - Series-At-Glance (13.61 KB)
9 - Section Intro (10.42 MB)
33 - Section Intro (18.95 MB)
34 - Reading In Data With readcsv (80.27 MB)
35 - Series Sizing With size shape And len (34.85 MB)
36 - Unique Values And Series Monotonicity (25.86 MB)
37 - The count Method (8.4 MB)
38 - Accessing And Counting NAs (54.16 MB)
39 - BONUS Another Approach (30.68 MB)
40 - The Other Side notnull And notna (16.49 MB)
41 - BONUS Booleans Are Literally Numbers In Python (16.58 MB)
42 - Skill Challenge (5.98 MB)
43 - Solution (20.31 MB)
44 - Dropping And Filling NAs (32.17 MB)
45 - Descriptive Statistics (46.64 MB)
46 - The describe Method (14.77 MB)
47 - mode And valuecounts (43.77 MB)
48 - idxmax And idxmin (32.37 MB)
49 - Sorting With sortvalues (29.15 MB)
50 - nlargest And nsmallest (17.53 MB)
51 - Sorting With sortindex (22.25 MB)
52 - Skill Challenge (4.57 MB)
53 - Solution (14.91 MB)
54 - Series Arithmetics And fillvalue (61.64 MB)
55 - BONUS Calculating Variance And Standard Deviation (25.04 MB)
56 - Cumulative Operations (26.59 MB)
57 - Pairwise Differences With diff (18.47 MB)
58 - Series Iteration (23.95 MB)
59 - Filtering filter where And mask (82.76 MB)
60 - Transforming With update apply And map (105.75 MB)
61 - Skill Challenge (15.66 MB)
62 - Solution I Reading Data (22.73 MB)
63 - Solution II Mean Median And Standard Deviation (30.35 MB)
64 - Solution III Zscores (73.57 MB)
65 - Series-Methods-And-Handling (31.84 KB)
100 - Another Skill Challenge (10.36 MB)
101 - Solution (56.37 MB)
102 - Working-With-DataFrames (105.51 KB)
66 - Section Intro (14.5 MB)
67 - What Is A DataFrame (67.99 MB)
68 - Creating A DataFrame (32.63 MB)
69 - BONUS Four More Ways To Build DataFrames (110.36 MB)
70 - The info Method (29.65 MB)
71 - Reading In Nutrition Data (40.97 MB)
72 - Some Cleanup Removing The Duplicated Index (55.16 MB)
73 - The sample Method (34.83 MB)
74 - BONUS Sampling With Replacement Or Weights (59.79 MB)
75 - BONUS How Are Random Numbers Generated (67.76 MB)
76 - DataFrame Axes (36.49 MB)
77 - Changing The Index (79.1 MB)
78 - Extracting From DataFrames By Label (53.51 MB)
79 - DataFrame Extraction by Position (71.9 MB)
80 - Single Value Access With at And iat (40.99 MB)
81 - BONUS The getloc Method (37.58 MB)
82 - Skill Challenge (5.89 MB)
83 - Solution (69.39 MB)
84 - More Cleanup Going Numeric (29.28 MB)
85 - The astype Method (38.45 MB)
86 - DataFrame replace A Glimpse At Regex (67.83 MB)
87 - Part I Collecting The Units (103.39 MB)
88 - The rename Method (40.74 MB)
89 - DataFrame dropna (58.48 MB)
90 - BONUS dropna With Subset (42.15 MB)
91 - Part II Merging Units With Column Names (86.99 MB)
92 - Part III Removing Units From Values (55.44 MB)
93 - Filtering in 2D (63.76 MB)
94 - DataFrame Sorting (77.58 MB)
95 - Using Series between With DataFrames (54.78 MB)
96 - BONUS Min Max and IdxMinMax And Good Foods (100.78 MB)
97 - DataFrame nlargest And nsmallest (56.31 MB)
98 - Skill Challenge (6.11 MB)
99 - Solution (66.09 MB)
103 - Section Intro (31.47 MB)
104 - Introducing A New Dataset (27.8 MB)
105 - Quick Review Indexing With Boolean Masks (35.04 MB)
106 - More Approaches To Boolean Masking (104.98 MB)
107 - Binary Operators With Booleans (55.73 MB)
108 - BONUS XOR and Complement Binary Ops (72.64 MB)
109 - Combining Conditions (70.44 MB)
110 - Conditions As Variables (29.15 MB)
111 - Skill Challenge (6.06 MB)
112 - Solution (61.28 MB)
113 - 2d Indexing (59.58 MB)
114 - Fancy Indexing With lookup (70.23 MB)
115 - Sorting By Index Or Column (69.56 MB)
116 - Sorting vs Reordering (99.54 MB)
117 - BONUS Another Way (20.11 MB)
118 - 15 BONUS Please Avoid Sorting Like This (26.2 MB)
119 - Skill Challenge (6.65 MB)
120 - Solution (39.84 MB)
121 - Identifying Dupes (92.68 MB)
122 - Removing Duplicates (45.55 MB)
123 - Removing DataFrame Rows (31.12 MB)
124 - BONUS Removing Columns (24.69 MB)
125 - BONUS Another Way pop (29.11 MB)
126 - BONUS A Sophisticated Alternative (51.56 MB)
127 - Null Values In DataFrames (65.46 MB)
128 - Dropping And Filling DataFrame NAs (74.69 MB)
129 - BONUS Methods And Axes With fillna (88.18 MB)
130 - Skill Challenge (7.95 MB)
131 - Solution (65.98 MB)
132 - Calculating Aggregates With agg (55.52 MB)
133 - Sameshape Transforms (102.21 MB)
134 - More Flexibility With apply (89.5 MB)
135 - Elementwise Operations With applymap (103.07 MB)
136 - Skill Challenge (13.61 MB)
137 - Solution (40.57 MB)
138 - Setting DataFrame Values (67.27 MB)
139 - The SettingWithCopy Warning (62.88 MB)
140 - View vs Copy (73.16 MB)
141 - Adding DataFrame Columns (55.42 MB)
142 - Adding Rows To DataFrames (77.09 MB)
143 - BONUS How Are DataFrames Stored In Memory (33.32 MB)
144 - Skill Challenge (7.28 MB)
145 - Solution (49.83 MB)
146 - DataFrames-In-Depth (59.45 KB)
146 - Slides (2.1 MB)
147 - Section Intro (10.49 MB)
148 - Introducing Five New Datasets (62.44 MB)
149 - Concatenating DataFrames (64.5 MB)
150 - The Duplicated Index Issue (79.42 MB)
151 - Enforcing Unique Indices (92.01 MB)
152 - BONUS Creating Multiple Indices With concat (43.68 MB)
153 - Column Axis Concatenation (43.03 MB)
154 - The append Method A Special Case Of concat (22.48 MB)
155 - Concat On Different Columns (60.15 MB)
156 - Skill Challenge (9.05 MB)
157 - Solution (91.8 MB)
158 - The merge Method (53.54 MB)
159 - The lefton And righton Params (50.52 MB)
160 - Inner vs Outer Joins (41.32 MB)
161 - Left vs Right Joins (31.46 MB)
162 - OnetoOne and OnetoMany Joins (89.97 MB)
163 - ManytoMany Joins (85.62 MB)
164 - Merging By Index (58.76 MB)
165 - The join Method (36.58 MB)
166 - Skill Challenge (5.75 MB)
167 - Solution (71.46 MB)
168 - Working-With-Multiple-DataFrames (27.38 KB)
169 - Section Intro (58.47 MB)
170 - Introducing New Data (34.14 MB)
171 - Index And RangeIndex (42.07 MB)
172 - Creating A MultiIndex (32.1 MB)
173 - MultiIndex From readcsv (43.61 MB)
174 - Indexing Hierarchical DataFrames (61.28 MB)
175 - Indexing Ranges And Slices (91.48 MB)
176 - BONUS Use With pdIndexSlice (25.5 MB)
177 - Cross Sections With xs (51 MB)
178 - Skill Challenge (5.53 MB)
179 - Solution (68.92 MB)
180 - The Anatomy Of A MultiIndex Object (52.94 MB)
181 - Adding Another Level (51.84 MB)
182 - Shuffling Levels (37.24 MB)
183 - Removing MultiIndex Levels (58.66 MB)
184 - MultiIndex sortindex (55.08 MB)
185 - More MultiIndex Methods (58.81 MB)
186 - Reshaping With stack (47.22 MB)
187 - The Flipside unstack (70.97 MB)
188 - BONUS Creating MultiLevel Columns Manually (91.34 MB)
189 - An Easier Way transpose (29.36 MB)
190 - BONUS What About Panels (43.02 MB)
191 - Skill Challenge (12.72 MB)
192 - Solution (76.03 MB)
193 - Going-MultiDimensional (41.94 KB)
194 - Section Intro (27.1 MB)
195 - New Data Game Sales (22.13 MB)
196 - Simple Aggregations Review (44.26 MB)
197 - Conditional Aggregates (37.42 MB)
198 - The SplitApplyCombine Pattern (33.47 MB)
199 - The groupby Method (33.52 MB)
200 - The DataFrameGroupBy Object (29.25 MB)
201 - Customizing Index To Group Mappings (30.91 MB)
202 - BONUS Series groupby (32.49 MB)
203 - Skill Challenge (4.7 MB)
204 - Solution (42.43 MB)
205 - Iterating Through Groups (31.68 MB)
206 - Handpicking Subgroups (35.86 MB)
207 - MultiIndex Grouping (40.53 MB)
208 - Finetuned Aggregates (67.87 MB)
209 - Named Aggregations (56.98 MB)
210 - The filter Method (40.64 MB)
211 - GroupBy Transformations (59.27 MB)
212 - BONUS Theres Also apply (63.26 MB)
213 - Skill Challenge (6 MB)
214 - Solution (37.7 MB)
215 - GroupBy-And-Aggregates ipynb (22.42 KB)
216 - Section Intro (38.75 MB)
217 - New Data New York City SAT Scores (40.89 MB)
218 - Pivoting Data (65.86 MB)
219 - Undoing Pivots (42.97 MB)
220 - What About Aggregates (53.89 MB)
221 - The pivottable (52.17 MB)
222 - BONUS The Problem With Average Percentage (55.09 MB)
223 - Replicating Pivot Tables With GroupBy (19.07 MB)
224 - Adding Margins (37.87 MB)
225 - MultiIndex Pivot Tables (30.4 MB)
226 - Applying Multiple Functions (27.97 MB)
227 - Skill Challenge (8.47 MB)
228 - Solution (56.77 MB)
229 - Reshaping-With-Pivots ipynb (17.15 KB)

Screenshot

Spoiler

​

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

 

[0dayddl]

The Ultimate Pandas Bootcamp: Advanced Python Data Analysis
Last updated 1/2024
Duration: 32h4m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 14.5 GB
Genre: eLearning | Language: English​

Master the powerful pandas library to analyze, manipulate and visualize data. More than 10 datasets & bonuses included!

What you'll learn
Learn everything there is to know about pandas - from absolute scratch!
Gain a deep and hands-on understanding of pandas data structures.
Transform, clean, filter, groupby, pivot, and otherwise manipulate a any dataset.
Understand related computer science topics like random-number generators, binary operators, memory pointers, and more!
Practice reading data from the web, pickles, Excel files right within pandas.
Discover and learn hundreds of methods, attributes, and techniques to manipulate data in pandas and python.

Requirements
A computer (Windows/Mac/Linux). That's all!
No prior knowledge of python is required.
No prior knowledge of pandas is required.
Description
Welcome to the
best resource
online for learning and mastering
data analysis with pandas
and python.
Over 32 hours, 10+ datasets, and 50+ skill challenges, you will gain hands-on mastery of, not only
pandas 1.x
, but also tens of computer science, statistics, and programming concepts.
We will break down, understand, and practice hundreds of methods, attributes, and techniques in pandas and python that will fundamentally change the way you work with data.
In
The Ultimate Pandas Bootcamp (2022)
you won't be working with outdated versions of pandas, writing repetitive commands on the same boring dataset. Instead, you'll learn
pandorable
and
pythonic
solutions to interesting, real-world data problems, while working with
many
diverse datasets that range from wine servings, video game sales, and SAT scores to stock prices, college salaries and more!
Data analysis is an applied science, which is why in each section,
you'll stop and practice what you learn
in dedicated skill challenges, followed by detailed solutions where we often consider and compare alternative solutions.
Data analysis is one of the most in-demand skill across all industries and an increasing number of roles. And python is increasingly the language of choice.
Pandas is the wonderful open-source library that is the embodiment of those trends: based on the python programming language, pandas is the de facto data analysis library in the python data science community.
----- Structure & Curriculum -----
Over more than 31 hours, we'll cover everything that pandas has to offer, from manipulating series and dataframes, to merging datasets, handling time series, aggregations, filtering, sorting and much more!
The first four sections of the bootcamp constitute the
core
curriculum. You'll get acquainted with series and dataframes and develop an in-depth understanding of pandas data structures.
· Series at a Glance
· Series Methods and Handling
· Introducing DataFrames
· DataFrames More In Depth
In the next eight sections, you will dive into more advanced topics and take your pandas skills to another level, learning how to work with multiple datasets, manipulate time series, visualize data, write custom functions to transform data and much more.
· Working With Multiple DataFrames
· Going MultiDimensional
· GroupBy And Aggregates
· Reshaping With Pivots
· Working With Dates And Time
· Regular Expressions And Text Manipulation
· Visualizing Data
· Data Formats And I/O
Pandas and python go hand-in-hand which is why this bootcamp also includes a
full-length introduction to the python programming language
, to get you up and running writing
pythonic
code in no time.
This is the
ultimate
course on one of the most-valuable skills today. I hope you commit to mastering data analysis with pandas.
See you inside!
Who this course is for:
Anyone looking to deeply understand and master pandas
Anyone interested in mastering data analysis with python

Bitte Anmelden oder Registrieren um Links zu sehen.

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

NitroFlare

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

DDownload

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!