"Data Analysis with Pandas and Python"-Ref.:Udemy.com | Data Analysis | Pandas | Python: Data Analysis with Pandas, Data Analysis with Python, Pandas, Python, Data Analysis

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"Data Analysis with Pandas and Python"-Ref.:Udemy.com
" Analyze data quickly and easily with Python's powerful pandas library! All datasets included --- beginners welcome! What Will I Learn? Perform a multitude of data operations in Python's popular "pandas" library including grouping, pivoting, joining and more! Learn hundreds of methods and attributes across numerous pandas objects Possess a strong understanding of manipulating 1D, 2D, and 3D data sets Resolve common issues in broken or incomplete data sets Curriculum For This Course Collapse All 174 Lectures 18:47:30 – Installation and Setup 02:08:50 Introduction to the Course Preview 12:15 Completed Course Files 00:25 Mac OS - Download the Anaconda Distribution Preview 03:28 Mac OS - Install Anaconda Distribution Preview 07:04 Mac OS - Access the Terminal 01:55 Mac OS - Update Anaconda Libraries 11:18 Mac OS - Unpack Course Materials + The Startdown and Shutdown Process 10:01 Windows - Download the Anaconda Distribution Preview 03:47 Windows - Install Anaconda Distribution Preview 05:16 Windows - Access the Command Prompt and Update Anaconda Libraries 10:11 Windows - Unpack Course Materials + The Startdown and Shutdown Process 08:49 Intro to the Jupyter Notebook Interface 05:14 Cell Types and Cell Modes 07:03 Code Cell Execution 04:47 Popular Keyboard Shortcuts 03:06 Import Libraries into Jupyter Notebook 07:09 Python Crash Course, Part 1 - Data Types and Variables 07:05 Python Crash Course, Part 2 - Lists 05:06 Python Crash Course, Part 3 - Dictionaries 04:19 Python Crash Course, Part 4 - Operators 04:30 Python Crash Course, Part 5 - Functions 06:02 – Series 02:06:12 Create Jupyter Notebook for the Series Module 02:12 Create A Series Object from a Python List Preview 10:32 Create A Series Object from a Python Dictionary 03:06 Intro to Attributes 07:17 Intro to Methods Preview 04:42 Parameters and Arguments 10:10 Import Series with the .read_csv() Method 10:23 The .head() and .tail() Methods 03:42 Python Built-In Functions 05:20 More Series Attributes 06:13 The .sort_values() Method Preview 06:04 The inplace Parameter 05:07 The .sort_index() Method 04:38 Python's in Keyword 04:00 Extract Series Values by Index Position 04:15 Extract Series Values by Index Label 07:22 The .get() Method on a Series 05:03 Math Methods on Series Objects 05:39 The .idxmax() and .idxmin() Methods 03:10 The .value_counts() Method 03:39 The .apply() Method 06:46 The .map() Method 06:52 A Review of the Series Module 7 questions – DataFrames I 01:36:37 Intro to DataFrames I Module 07:24 Shared Methods and Attributes between Series and DataFrames 07:37 Differences between Shared Methods 06:48 Select One Column from a DataFrame Preview 07:57 Select Two or More Columns from a DataFrame 05:12 Add New Column to DataFrame 08:03 Broadcasting Operations 09:07 A Review of the .value_counts() Method 03:54 Drop Rows with Null Values 06:41 Fill in Null Values with the .fillna() Method 04:25 The .astype() Method 10:38 Sort a DataFrame with the .sort_values() Method, Part I Preview 05:46 Sort a DataFrame with the .sort_values() Method, Part II 04:13 Sort DataFrame with the .sort_index() Method 02:59 Rank Values with the .rank() Method 05:53 – DataFrames II 01:16:56 This Module's Dataset + Memory Optimization 10:45 Filter a DataFrame Based on A Condition Preview 12:57 Filter with More than One Condition (AND - &) 04:41 Filter with More than One Condition (OR - |) 08:35 The .isin() Method 06:17 The .isnull() and .notnull() Methods 05:07 The .between() Method 06:51 The .duplicated() Method 09:05 The .drop_duplicates() Method 08:16 The .unique() and .nunique() Methods 04:22 – DataFrames III 01:52:12 Intro to the DataFrames III Module + Import Dataset 03:23 The .set_index() and .reset_index() Methods 05:37 Retrieve Rows by Index Label with .loc[] 09:43 Retrieve Rows by Index Position with .iloc[] 06:07 The Catch-All .ix[] Method 08:44 Second Arguments to .loc[], .iloc[], and .ix[] Methods 06:21 Set New Values for a Specific Cell or Row 04:27 Set Multiple Values in DataFrame 09:16 Rename Index Labels or Columns in a DataFrame Preview 06:49 Delete Rows or Columns from a DataFrame 07:29 Create Random Sample with the .sample() Method 04:43 The .nsmallest() and .nlargest() Methods 05:36 Filtering with the .where() Method 05:03 The .query() Method 09:07 A Review of the .apply() Method on Single Columns 05:53 The .apply() Method with Row Values 06:49 The .copy() Method 07:05 – Working with Text Data 59:42 Intro to the Working with Text Data Module 05:55 Common String Methods - lower, upper, title, and len Preview 07:14 The .str.replace() Method 08:07 Filtering with String Methods 06:43 More String Methods - strip, lstrip, and rstrip 04:31 String Methods on Index and Columns 05:30 Split Strings by Characters with .str.split() Method 08:41 More Practice with Splits 06:01 The expand and n Parameters of the .str.split() Method 07:00 – MultiIndex 01:30:52 Intro to the MultiIndex Module 04:26 Create a MultiIndex with the set_index() Method 09:50 The .get_level_values() Method 07:52 The .set_names() Method 03:08 The sort_index() Method 04:56 Extract Rows from a MultiIndex DataFrame 08:32 The .transpose() Method and MultiIndex on Column Level 05:48 The .swaplevel() Method 02:34 The .stack() Method Preview 06:01 The .unstack() Method, Part 1 03:38 The .unstack() Method, Part 2 06:09 The .unstack() Method, Part 3 05:09 The .pivot() Method 06:34 The .pivot_table() Method 10:16 The pd.melt() Method Preview 05:59 – GroupBy 49:33 Intro to the Groupby Module 07:42 First Operations with groupby Object 09:33 Retrieve A Group with the .get_group() Method 03:47 Methods on the Groupby Object and DataFrame Columns 08:41 Grouping by Multiple Columns 04:35 The .agg() Method Preview 06:11 Iterating through Groups 09:04 – Merging, Joining, and Concatenating 01:26:22 Intro to the Merging, Joining, and Concatenating Module 05:47 The pd.concat() Method, Part 1 05:39 The pd.concat() Method, Part 2 06:35 The .append() Method on a DataFrame 02:03 Inner Joins, Part 1 09:18 Inner Joins, Part 2 09:01 Outer Joins 12:23 Left Joins Preview 09:19 The left_on and right_on Parameters 08:54 Merging by Indexes with the left_index and right_index Parameters 11:02 The .join() Method 03:15 The pd.merge() Method 03:06 – Working with Dates and Times 02:27:44 Intro to the Working with Dates and Times Module 03:44 Review of Python's datetime Module 09:31 The pandas Timestamp Object 07:15 The pandas DateTimeIndex Object 05:23 The pd.to_datetime() Method 11:11 Create Range of Dates with the pd.date_range() Method, Part 1 Preview 10:22 Create Range of Dates with the pd.date_range() Method, Part 2 09:04 Create Range of Dates with the pd.date_range() Method, Part 3 07:50 The .dt Accessor 07:29 Install pandas-datareader Library 02:30 Import Financial Data Set with pandas_datareader Library 10:43 Selecting Rows from a DataFrame with a DateTimeIndex 08:01 Timestamp Object Attributes 07:27 The .truncate() Method 02:59 pd.DateOffset Objects 12:00 More Fun with pd.DateOffset Objects 14:06 The pandas Timedelta Object 08:39 Timedeltas in a Dataset 09:30 - Panels 59:17 Intro to the Module + Fetch Panel Dataset from Google Finance 07:17 The Axes of a Panel Object 07:42 Panel Attributes Preview 05:04 Use Bracket Notation to Extract a DataFrame from a Panel 03:59 Extracting with the .loc, .iloc, and .ix Methods 06:57 Convert Panel to a MultiIndex DataFrame (and Vice Versa) 04:04 The .major_xs() Method 05:46 The .minor_xs() Method 06:24 Transpose a Panel with the .transpose() Method 07:42 The .swapaxes() Method 04:22 A Review of the Panels Module 5 questions - Input and Output 37:02 Intro to the Input and Output Module 01:33 Feed pd.read_csv() Method a URL Argument Preview 03:48 Quick Object Conversions 05:04 Export DataFrame to CSV File with the .to_csv() Method 05:49 Install xlrd and openpyxl Libraries to Read and Write Excel Files 02:36 Import Excel File into pandas 09:30 Export Excel File 08:42 Input and Output 3 questions - Visualization 36:29 Intro to Visualization Module 04:16 The .plot() Method Preview 09:13 Modifying Aesthetics with Templates 05:20 Bar Graphs 06:24 Pie Charts 05:07 Histograms 06:09 Visualization 5 questions - Options and Settings 18:03 Introduction to the Options and Settings Module 01:42 Changing pandas Options with Attributes and Dot Syntax 06:57 Changing pandas Options with Methods 06:14 The precision Option 03:10 - Conclusion 01:39 Conclusion 01:39 " - Ref.: Udemy.com 
URL: Free-Udemy-Coupon-Code-Gratis-Gratuit-Frei-Gratuito-Kupon-Libre-Gabay-Ders-Bedava-Online-Course-Web-Deal-Discount-Red-Tag-Sale Reference: https://www.udemy.com/data-analysis-with-pandas/?couponCode=FREEPANDAS1
   


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