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‎README.md

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@@ -31,35 +31,22 @@ In Pandas, the data is usually utilized to support statistical analysis in SciPy
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## Main Features
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Here are just a few of the things that pandas does well:
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- Easy handling of [**missing data**][missing-data] (represented as
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`NaN`) in floating point as well as non-floating point data
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- Size mutability: columns can be [**inserted and
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deleted**][insertion-deletion] from DataFrame and higher dimensional
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objects
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- Automatic and explicit [**data alignment**][alignment]: objects can
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be explicitly aligned to a set of labels, or the user can simply
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ignore the labels and let `Series`, `DataFrame`, etc. automatically
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align the data for you in computations
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- Powerful, flexible [**group by**][groupby] functionality to perform
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split-apply-combine operations on data sets, for both aggregating
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- Easy handling of [**missing data**][missing-data] (represented as `NaN`) in floating point as well as non-floating point data
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- Size mutability: columns can be [**inserted and deleted**][insertion-deletion] from DataFrame and higher dimensional objects
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- Automatic and explicit [**data alignment**][alignment]: objects can be explicitly aligned to a set of labels, or the user can simply
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ignore the labels and let `Series`, `DataFrame`, etc. automatically align the data for you in computations
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- Powerful, flexible [**group by**][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating
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and transforming data
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- Make it [**easy to convert**][conversion] ragged,
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differently-indexed data in other Python and NumPy data structures
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- Make it [**easy to convert**][conversion] ragged, differently-indexed data in other Python and NumPy data structures
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into DataFrame objects
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- Intelligent label-based [**slicing**][slicing], [**fancy
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indexing**][fancy-indexing], and [**subsetting**][subsetting] of
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- Intelligent label-based [**slicing**][slicing], [**fancy indexing**][fancy-indexing], and [**subsetting**][subsetting] of
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large data sets
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- Intuitive [**merging**][merging] and [**joining**][joining] data
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sets
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- Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
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data sets
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- [**Hierarchical**][mi] labeling of axes (possible to have multiple
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labels per tick)
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- Robust IO tools for loading data from [**flat files**][flat-files]
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(CSV and delimited), [**Excel files**][excel], [**databases**][db],
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- Intuitive [**merging**][merging] and [**joining**][joining] datasets
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- Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of datasets
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- [**Hierarchical**][mi] labeling of axes (possible to have multiple labels per tick)
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- Robust IO tools for loading data from [**flat files**][flat-files] (CSV and delimited), [**Excel files**][excel], [**databases**][db],
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and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
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- [**Time series**][timeseries]-specific functionality: date range
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generation and frequency conversion, moving window statistics,
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- [**Time series**][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics,
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moving window linear regressions, date shifting and lagging, etc.
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## Table of contents 📋
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[001_Python_Pandas_DataFrame](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_DataFrame.ipynb)
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- [001_Python_Pandas_DataFrame_from_Dictionary](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/001_Python_Pandas_DataFrame_from_Dictionary.ipynb)
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- [002_Python_Pandas_DataFrame_from_List](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/002_Python_Pandas_DataFrame_from_List.ipynb)
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- [003_Python_Pandas_DataFrame_head()_and_tail()](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/003_Python_Pandas_DataFrame_head()_and_tail().ipynb)
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- [004_Python_Pandas_DataFrame_drop_columns](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/004_Python_Pandas_DataFrame_drop_columns.ipynb)
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- [005_Python_Pandas_DataFrame_drop_duplicates](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/005_Python_Pandas_DataFrame_drop_duplicates.ipynb)
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- [006_Python_Pandas_DataFrame_drop_columns_with_NA](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/006_Python_Pandas_DataFrame_drop_columns_with_NA.ipynb)
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- [007_Python_Pandas_DataFrame_rename_columns](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/007_Python_Pandas_DataFrame_rename_columns.ipynb)
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- [008_Python_Pandas_DataFrame_to_Python_dictionary](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/008_Python_Pandas_DataFrame_to_Python_dictionary.ipynb)
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- [009_Python_Pandas_DataFrame_set_index](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/009_Python_Pandas_DataFrame_set_index.ipynb)
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- [010_Python_Pandas_DataFrame_reset_index](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/010_Python_Pandas_DataFrame_reset_index.ipynb)
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[002_Python_Pandas_Exercise_1](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/002_Python_Pandas_Exercise_1.ipynb)
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[003_Python_Pandas_Exercise_2](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/003_Python_Pandas_Exercise_2.ipynb)
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[automobile_data.csv](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/automobile_data.csv)
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[pokemon_data.csv](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/pokemon_data.csv)
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[Pandas Cheat Sheet Data Wrangling in Python.pdf](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/Pandas%20Cheat%20Sheet%20Data%20Wrangling%20in%20Python.pdf)
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[Pandas Cheat Sheet for Data Science in Python.pdf](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/Pandas%20Cheat%20Sheet%20for%20Data%20Science%20in%20Python.pdf)
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These are online read-only versions.
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| **No.** | **Name** |
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| ------- | -------- |
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| 01 | **[Python_Pandas_DataFrame](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_DataFrame.ipynb)** |
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| | 1.1 **[001_Python_Pandas_DataFrame_from_Dictionary](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/001_Python_Pandas_DataFrame_from_Dictionary.ipynb)** |
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| | 1.2 **[Python_Pandas_DataFrame_from_List](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/002_Python_Pandas_DataFrame_from_List.ipynb)** |
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| | 1.3 **[Python_Pandas_DataFrame_head()and_tail()](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/003_Python_Pandas_DataFrame_head()_and_tail().ipynb)** |
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| | 1.4 **[004_Python_Pandas_DataFrame_drop_columns](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/004_Python_Pandas_DataFrame_drop_columns.ipynb)** |
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| | 1.5 **[Python_Pandas_DataFrame_drop_duplicates](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/005_Python_Pandas_DataFrame_drop_duplicates.ipynb)** |
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| | 1.6 **[Python_Pandas_DataFrame_drop_columns_with_NA](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/006_Python_Pandas_DataFrame_drop_columns_with_NA.ipynb)** |
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| | 1.7 **[Python_Pandas_DataFrame_rename_columns](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/007_Python_Pandas_DataFrame_rename_columns.ipynb)** |
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| | 1.8 **[Python_Pandas_DataFrame_to_Python_dictionary](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/008_Python_Pandas_DataFrame_to_Python_dictionary.ipynb)** |
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| | 1.9 **[Python_Pandas_DataFrame_set_index](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/009_Python_Pandas_DataFrame_set_index.ipynb)** |
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| | 1.10 **[Python_Pandas_DataFrame_reset_index](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/001_Python_Pandas_Methods/010_Python_Pandas_DataFrame_reset_index.ipynb)** |
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| 02 | **[Python_Pandas_Exercise_1](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/002_Python_Pandas_Exercise_1.ipynb)
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| 03 | **[Python_Pandas_Exercise_2](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/003_Python_Pandas_Exercise_2.ipynb)
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| | **[automobile_data.csv](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/automobile_data.csv)
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| | **[pokemon_data.csv](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/pokemon_data.csv)
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| 04 | **[Pandas Cheat Sheet Data Wrangling in Python.pdf](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/Pandas%20Cheat%20Sheet%20Data%20Wrangling%20in%20Python.pdf)
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| 05 | **[Pandas Cheat Sheet for Data Science in Python.pdf](https://github.com/milaan9/10_Python_Pandas_Module/blob/main/Pandas%20Cheat%20Sheet%20for%20Data%20Science%20in%20Python.pdf)
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These are online **read-only** versions. However you can "Run ▶" the code **online** by clicking here &#8594; <a href="https://mybinder.org/v2/gh/milaan9/10_Python_Pandas_Module/HEAD"><img src="https://img.shields.io/badge/Open in-binder-579aca.svg?logo=data:image/png;base64,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" alt="binder"/></a>
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