There are instances where we have to select the rows from a Pandas dataframe by multiple conditions. Especially, when we are dealing with the text data then we may have requirements to select the rows matching a substring in all columns or select the rows based on the condition derived by concatenating two column values and many other scenarios where you have to slice,split,search substring with the text data in a Pandas Dataframe.
Here we are going to discuss following unique scenarios for dealing with the text data:.
We will now select all the rows which have following list of values ville and Aura in their city Column. After executing the above line of code it gives the following rows containing ville and Aura string in their City name. We will see how we can select the rows by list of indexes. Now we will select all the rows which has Age in the following list: 20,30 and 25 and then reset the index. The name column in this dataframe contains numbers at the last and now we will see how to extract those numbers from the string using extract function.
We will use regular expression to locate digit within these name values. We can see all the number at the last of name column is extracted using a simple regular expression. In the above section we have seen how to extract a pattern from the string and now we will see how to strip those numbers in the name. We will split these characters into multiple columns. The Pahun column is split into three different column i. The string indexing is quite common task and used for lot of String operations.
This will give all the values which have Grade A so the result will be a series with all the matching patterns in a list. We have seen situations where we have to merge two or more columns and perform some operations on that column.
So you have seen Pandas provides a set of vectorized string functions which make it easy and flexible to work with the textual data and is an essential part of any data munging task. These functions takes care of the NaN values also and will not throw error if any of the values are empty or null. There are many other useful functions which I have not included here but you can check their official documentation for it. Hi, Just a comment on a typo: df[df.
Thanks a lot.
Your email address will not be published. Facebook 0 Tweet 0 Pin 0 LinkedIn 0. Leave a Reply Cancel reply Your email address will not be published.Enter search terms or a module, class or function name. Series and Index are equipped with a set of string processing methods that make it easy to operate on each element of the array. These are accessed via the str attribute and generally have names matching the equivalent scalar built-in string methods:. The string methods on Index are especially useful for cleaning up or transforming DataFrame columns.
For instance, you may have columns with leading or trailing whitespace:. Since df. These string methods can then be used to clean up the columns as needed. Here we are removing leading and trailing whitespaces, lowercasing all names, and replacing any remaining whitespaces with underscores:. If you have a Series where lots of elements are repeated i. The performance difference comes from the fact that, for Series of type categorythe string operations are done on the. Please note that a Series of type category with string.
Methods like split return a Series of lists:. Elements in the split lists can be accessed using get or  notation:. It is easy to expand this to return a DataFrame using expand. Some caution must be taken to keep regular expressions in mind!
If you do want literal replacement of a string equivalent to str. In this case both pat and repl must be strings:.
The replace method can also take a callable as replacement. It is called on every pat using re. The callable should expect one positional argument a regex object and return a string. The replace method also accepts a compiled regular expression object from re.
All flags should be included in the compiled regular expression object. Including a flags argument when calling replace with a compiled regular expression object will raise a ValueError. There are several ways to concatenate a Series or Indexeither with itself or others, all based on catresp. The content of a Series or Index can be concatenated:. By default, missing values are ignored. The first argument to cat can be a list-like object, provided that it matches the length of the calling Series or Index.There are several pandas methods which accept the regex in pandas to find the pattern in a String within a Series or Dataframe object.
These methods works on the same line as Pythons re module. Its really helpful if you want to find the names starting with a particular character or search for a pattern within a dataframe column or extract the dates from the text. First create a dataframe if you want to follow the below examples and understand how regex works with these pandas function.
Replaces all the occurence of matched pattern in the string. We want to remove the dash - followed by number in the below pandas series object. The output is list of countres without the dash and number. It calls re. It uses re. We just need to filter all the True values that is returned by contains function.
This is equivalent to str. Here we are splitting the text on white space and expands set as True splits that into 3 different columns. We have seen how regexp can be used effectively with some the Pandas functions and can help to extract, match the patterns in the Series or a Dataframe. Especially when you are working with the Text data then Regex is a powerful tool for data extraction, Cleaning and validation.
You can just skip part when you compare with True for functions that return boolean values i. Happened to me when using pandas.
Your email address will not be published. Calls re. Equivalent to applying re. Total items starting with F S. Total items starting with F. Get countries starting with letter P. Remove the dash - followed by number from all countries in the Series.
Series ["StatueofLiberty built-on Oct"] s. Series [ "StatueofLiberty built-on Oct" ]. Facebook 0 Tweet 0 Pin 0 LinkedIn 0. Leave a Reply Cancel reply Your email address will not be published.
Learn more. Asked 3 years, 10 months ago. Active 1 year, 7 months ago. Viewed 28k times.
The desired result is: A 0 1 1 NaN 2 10 3 4 0 I know it can be done with str. Dance Party Dance Party 2, 3 3 gold badges 24 24 silver badges 45 45 bronze badges. Active Oldest Votes. Give it a regex capture group: df. StevenG strip out commas first? To answer Steven G 's question in the comment above, this should work: df.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Learn more. Asked 3 years, 10 months ago. Active 1 year, 6 months ago. Viewed 28k times. The desired result is: A 0 1 1 NaN 2 10 3 4 0 I know it can be done with str.
Dance Party Dance Party 2, 3 3 gold badges 24 24 silver badges 45 45 bronze badges. Active Oldest Votes. Give it a regex capture group: df.
StevenG strip out commas first? To answer Steven G 's question in the comment above, this should work: df. Taming Taming 51 5 5 bronze badges.
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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I want to use split ":" and iteration so that I can convert the string into a dictionary on row level first and then to manipulate and feed those columns with the target information.
Learn more. Split string into separate columns with target information by pandas Ask Question. Asked 2 days ago. Active yesterday. Viewed 35 times. Question re-edited: I have a pandas series something like below: "number of pages: 80 posted: Mar 03 last revised: May 05 " "number of pages: posted: Feb 02 " "posted: Jul 20 " I would like to split the pandas series into 3 columns as below: number of pages posted date last revised date 80 Mar 03 May 05 Feb 02 Feb 02 Nan Jul 20 Nan I want to use split ":" and iteration so that I can convert the string into a dictionary on row level first and then to manipulate and feed those columns with the target information.
Though page numbers end up with 1 to 3 digits. R-Nie R-Nie 1 1 1 bronze badge. New contributor. You need to describe your code and the dataset in text.
You can't just take a screenshot and ask people to write the code for you. This is the first time I posted question here, sorry if it looks like I am simply asking for a solution from people.
I tried to upload my csv file but failed.
I am editing my post based on your reply now. I would look into splitting the column on a regular expression, if there is a pattern like number of pages Take a look at this question for more details stackoverflow. Thank you. I originally tried to split into columns based on different delimiter, like ":" and white space.
Problem is each row has different length, several rows come without information like number of pages or posted date. I assume it will be a headache if I web scrape multiple pages. I want to use extract though, but I don't understand part of the documentation.
I made my question more specific, hope it makes sense for anyone who'd like to help me out. That's regex. Can you clarify your question?Many times, while working with strings we come across this issue in which we need to get all the numeric occurrences. This type of problem generally occurs in competitive programming and also in web development.
Method 2 : Using re. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below. Writing code in comment? Please use ide. Python3 code to demonstrate. Output : The original string : There are 2 apples for 4 persons The numbers list is : [2, 4]. Recommended Posts: Extract numbers from a text file and add them using Python Python Extract numbers from list of strings Python Extract words from given string Python Extract digits from given string Python Extract only characters from given string Python Extract Score list of String Python Regex to extract maximum numeric value from a string Python Extract length of longest string in list Python Extract suffix after K numpy.
Check out this Author's contributed articles. Load Comments.Use Regular Expression to split string into Dataframe columns (Pandas)