## Part 11: VECTORIZED STRING OPERATIONS

We will learn to apply the famous Python string operations on Pandas Series and DataFrame objects

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# Tag: Pandas

## Part 11: VECTORIZED STRING OPERATIONS

## Part 10: PIVOT TABLES

## Part 9: AGGREGATION AND GROUPING

## Part 8: FILTERING AND SORTING IN PANDAS

## Part 7: COMBINING DATASETS IN PANDAS

## Part 6: HIERARCHICAL INDEXING IN PANDAS

## Part 5: HANDLING MISSING DATA IN PANDAS

## Part 4: UNIVERSAL FUNCTIONS IN PANDAS

## Part 3: I/O FILE READ and WRITE

## Part 2: INDEXING PANDAS SERIES AND DATAFRAME

Finance & Tech

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We will learn to apply the famous Python string operations on Pandas Series and DataFrame objects

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We will go through the basics of working of Pivot Tables – essentially a multi-dimensional version of GroupBy aggregation

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Learn the simple aggregation techniques along with Pandas GroupBy function applied on Series and DataFrame.

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In this article, we will learn how to apply various filtering and sorting techniques on Pandas Series and DataFrame

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In this article, we will learn how to combine datasets using concat, append, merge and join functions/methods

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In this article, we will learn how to create, index, slice, rearrange and aggregate a multi-index Series and DataFrame.

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In this article, we will learn how to handle the missing data in Pandas, which includes detecting, dipping and filling the missing data.

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We will cover various Universal functions in Pandas

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In this article, we will learn the basics of I/O: how to read/write files in csv, json and xls format

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In this article, we will learn how to index Pandas Series and DataFrame, including fancy indexing and using indexers like loc and iloc