Python Pandas: Data analysis. Pivot table. Pandas data visualization tools. Work with sqlalchemy.
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00:00 What is pandas Data analytics
00:42 Install Anaconda distribution on Mac and Ubuntu
00:32 The main data structures in pandas: Series and DataFrame
07:42 The difference between np.array and pandas Series
08:13 Creating a DataFrame from different data structures
09:52 Creating a Series from different data structures
10:55 Adding two series in pandas library
12:50 Work with DataFrame columns. Getting, creating and deleting columns in a pandas DataFrame
14:26 Shape function
14:34 Getting the rows in DataFrame. loc and iloc
15:05 Getting the subset of the rows and the columns in pandas DataFrame
15:30 Getting the subset of data with certain conditions
16:28 reset_index
16:41 set_index
17:12 Hierarchical indexing (MultiIndex)
23:10 Filtering, grouping, aggregation and sorting DataFrame
30:48 Where to get real data for practice?
32:51 Load data we downloaded. read_csv method
33:59 head method. View numbers of rows from the beginning
34:13 tail. View specific numbers of rows from the end
34:31 info method. Getting an overview of the DataFrame
34:53 describe method
35:16 Work with sqlalchemy. Saving and reading the DataFrame. to_sql and read_sql methods
39:16 isnull command. Detect missing values
39:43 fillna. Replacing the NULL values with a specified value.
40:04 processing columns of the DataFrame
42:21 dropna deleting columns and rows
43:06 unique and nunique methods
43:37 value_counts method
44:08 pivot_table Creating a spreadsheet-style pivot table as a DataFrame.
45:36 Pandas data visualization tools
48:35 hist. Building histogram for column
49:03 scatter plot
49:34 pie plot
50:18 box plot
51:22 area plot
51:54 hexbin plot
52:35 scatter matrix plot
In this tutorial for beginners You'll Learn:
How to install Anaconda on Ubuntu and Mac for a seamless Python environment setup.
Pandas Basics:
Introduction to Pandas and its essential data structures: Series and DataFrame.
The difference between arrays and Pandas Series.
DataFrame Essentials:
What is a DataFrame?
How to create DataFrames from scratch.
Techniques for reading data into DataFrames using read_csv and read_sql.
Advanced Data Handling:
Introduction to hierarchical multi-indexing for complex data analysis.
Practical examples of multi-indexing in Pandas to manage and analyze multi-dimensional data.
Data Manipulation:
Using pivot tables in Pandas for data summarization and analysis.
#PandasPython #DataScience #PythonPandasTutorial #HierarchicalMultiIndex #DataStructuresInPandas #WorkWithDataFrame #CreateDataFramePandas #CreateSeriesPandas #ReadCSV #PivotTablePandas #PandasReadSQL #InstallAnaconda #dataanalysistools #dataanalysis
En esta página del sitio puede ver el video en línea Python Pandas: Data analysis. Pivot table. Pandas data visualization tools. Work with sqlalchemy. de Duración hora minuto segunda en buena calidad , que subió el usuario Digital Program Life 27 mayo 2024, comparta el enlace con amigos y conocidos, en youtube este video ya ha sido visto 2,858 veces y le gustó 79 a los espectadores. Disfruta viendo!
