Convert Column to String in Pandas

Introduction

The act of altering, parsing, splicing, pasting, or analyzing strings (or string methods) is known as string manipulation. As is common knowledge, sometimes the data included in a string cannot be used to manipulate an analysis or obtain a description of the data. However, Python is renowned for its various string methods in pandas. We will thus learn how Pandas gives us the ability to edit, change, and process string data-frames using certain built-in methods by expanding it here. Some of the built-in functions in the Pandas library are frequently used to manipulate strings in data frames.

We will first learn how to use pandas to construct a string data-frame by defining dataframe string create the pandas series, and pandas convert the column to a string:

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Common string Methods in Pandas

Let's analyze a few more string manipulation methods that this library provides.

• "upper()"

  The function returns the uppercase strings as a result after changing all lowercase letters in the DataFrame to uppercase. Code:

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• "lower()"

  Lowercase strings are produced by changing all capital letters in the DataFrame's strings to lowercase.

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• ###capitalize() We are utilizing the pandas Series.str.capitalize() function, which enables us to capitalize the initial letter of a given series while leaving the other characters intact.

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• "title()"

  The Series/Index method str.title() is used to titlecase convert string. The method is comparable to str.title().

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• "swapcase()"

  It reverses the case from lower to upper and vice versa. It does this for each string, converting all uppercase letters into lowercase and vice versa (lowercase -> uppercase), as seen in the sample below.

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• "contains()"

  On accessing the series' values as strings and perform various operations to them, use the Series.str function. The Pandas Series.str.contains() method is used to determine whether a pattern or regex is present inside a Series or Index string. The function determines if a particular pattern or regex is present in a string of a Series or Index and returns a boolean Series or Index based on that determination.

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To determine if a pattern is present in the string that makes up the underlying data of the supplied series object, we will now utilize the Series.str.contains() method.

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• "find()"

  The initial place of the pattern's first occurrence is returned. In the sample below, we can see that it returns the index value of the appearance of the character 'n' in each string across the DataFrame.

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• "replace()"

  In the example below, "Google" is being replaced by "Microsoft" using the replace(a,b) function. Code:

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• "strip()"

  We should use strip() a string method to trim strings that have additional spaces at the beginning or end of them, or we may remove the excess spaces that a string in a DataFrame has.

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• "split()"

  With the help of a provided separator or delimiter, Pandas offers a means to separate strings. Following that, the string may be kept as a list in a series or it may be used to construct data frames with several columns from a single separated string. The only difference between it and Python's standard split() technique is that it only operates on a single string.

The .str prefix must always be used before invoking this method to distinguish it from Python's default function; otherwise, an error will be thrown. The Pandas str.split() method may be used to split an entire series. 

• Splitting as a list

The string is split into a list The Gender column in this data is divided using the split function every "e." Because the option is set to 1, the maximum number of separations in a single string will be 1. Since the expand argument is False, a series with a list of strings rather than a data frame is returned.

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• Splitting multiple columns

This example will produce a data frame with all separated strings organized into distinct columns since the Gender column is separated at spaces (" ") and the expand option is set to True. The old Name column is then deleted using the .drop() function, and new columns are subsequently added to the Data frame. Code:

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• "startswith()"

If the element or string in the DataFrame Index starts with the pattern, it returns true.

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• "endswith()"

If the string or element in the DataFrame Index ends with the pattern, it returns true.

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The isin() Function

Data frames are filtered using the isin() function of Pandas. The isin() function facilitates choosing rows with a certain (or many) values in a specific column. The following example checks the rows and returns a boolean series that is True whenever Gender="Male". When the new, filtered data frame is ready, the series is then handed to it.

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The astype() Function

When we wish to convert a certain column data type to another data type, the DataFrame.astype() method is really helpful. Additionally, we may alter many column types at once by using a Python dictionary input. The dictionary's key label corresponds to the name of the column, and the values label to the new data types we want the columns to contain.

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