Python Data Analysis Tools: Data processing Pandas vs Polars. Lazy evaluation vs eager mode

Two Big Data Analysis Tools: Pandas vs. Polars. What's the best for you?
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00:00 What polars is?
01:06 Polars Data Analysis. Main features
03:37 install polars
04:52 read_csv Load data file. View shape of the DataFrame
05:37 view only part of data. head
06:04 Convert polars DataFrame into Pandas DataFrame. to_pandas
06:32 Load Data Sets from git hub directly
07:43 Polars Series. Methods to work with polars Series: get_column, to_series,
10:10 Arithmetic operations in polars. Comparison operations.
12:37 Mask values: is_between.
13:01 Create new column with_column
14:15 Delete a column. Drop function
14:49 Filter series
15:44 Concatenating two series
17:21 Work with a DataFrame. Index syntax in polars. Slicing
18:14 Describe method
18:40 Estimated size memory usage
19:10 Explore the DataFrame with Polars: is_duplicated, is_empty, is_unique, n_unique, null_count, count.
21:21 Data analysis with functions: mean_horizontal, min, max, product, var, std
23:35 Work with the structure of the DataFrame: flags, columns, schema, width, glimpse, n_chunks
26:38 to_arrow Converting polars data frame to apache arrow format
27:00 Convert polars dataFrame to different formats: to_dict, to_dicts, to_init_repr, to_numpy, to_pandas, to_torch
29:10 Group_by method
31:50 map_groups (apply) function
33:50 View the last entry in the date frame. tail()
35:01 Join strategies: inner, left, full, cross, semi, anti
38:30 Pivot table in polars
41:25 equals(). Comparing DataFrames
42:42 write polars DataFrame into the file. write_csv()
42:59 Eager and Lazy modes in Polars: scan_csv.
44:33 %%timeit magic command to measure time for loading datasets
50:16 Read big data Pandas vs Polars. Comparing Pandas loading DataFrame with Polars loading.
50:32 Managing out of memory situation with Pandas. Chunks reading.
51:47 %memit magic command to measure memory usage. memory_profiler.
52:42 Scan_csv for large data. Lazy evaluation vs eager mode. Show_graph(). LazyFrame collect. Polars streaming data processing.
57:21 Data visualisation in polars.
59:39 Polars limitations. What difficulties might you encounter?

DataFrames I worked with:
🔗 https://www.kaggle.com/datasets/zanji...
🔗 GitHub https://github.com/RandomFractals/chi...
🔗 Or raw link for usage https://raw.githubusercontent.com/Ran...

#PolarsTutorial #LearnPolars #PolarsDataAnalysis #DataScience #PolarsDataFrame #PythonPolars #DataProcessing #DataManipulation #BigData #DataAnalysis #DataEngineering #MachineLearning #dataanalysistools #DataFrameOperations #EfficientDataLoading