String Operations on Pandas DataFrame

String Methods

In this article, let’s cover the string operations that can be performed on pandas dataframe.

1. Converting to uppercase/lowercase/titlecase — str. upper, str. lower, str. title
2. strip,lstrip, rstrip
3. split
4. replace
5. startswith,endswith, contains

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1. Converting to uppercase/lowercase/title case

Example 1: Converting strings in a column in pandas dataframe to uppercase

s1=pd.read_csv("s1.csv")
s1.head()

s1['Place']=s1.Place.str.upper()
s1.head()

All values in the “Place” columns are converted to uppercase.

Example 2: Converting strings in a column in pandas dataframe to lowercase

s1['Place']=s1.Place.str.lower()
s1.head()

Example 3: Converting strings in a column in pandas dataframe to titlecase

s1['Place']=s1.Place.str.title()
s1.head()

2. strip,lstrip,rstrip

Example 1: Let’s strip ‘$’ from the “Salary” column. Both leading and trailing ‘$’ sign.

strip → used to remove leading and trailing whitespaces/character mentioned.

s1['Salary']=s1.Salary.str.strip('$')
s1.head()

Example 2: Let’s strip leading ‘$’ from the “Salary” column.

lstrip → used to remove leading whitespaces/character mentioned.

s1['Salary']=s1.Salary.str.lstrip('$')
s1.head()

Example 3: Let’s strip the trailing ‘$’ symbol from the “Salary” column.

rstrip → used to remove trailing whitespaces/character mentioned.

s1['Salary']=s1.Salary.str.rstrip('$')
s1.head()

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

s1[['City','State']]=s1.Place.str.split(',',expand=True)
s1.head()

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

s1['State']=s1.State.str.replace('nc','North Carolina')
s1.head()

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

s2=s1.loc[s1.Place.str.startswith("C")]
s2

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

s2=s1.loc[s1.Place.str.endswith("as")]
s2

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

s2=s1.loc[s1.Place.str.contains("lotte")]
s2

---

References:

https://pandas.pydata.org/pandas-docs/version/0.22/api.html#string-handling

Thanks for reading!

---

If you like to read more of my tutorials on Python and Data Science,
follow me on medium, Twitter

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)
s1.head()

Example 2: Let’s strip leading ‘$’ from the “Salary” column.

lstrip → used to remove leading whitespaces/character mentioned.

Example 3: Let’s strip the trailing ‘$’ symbol from the “Salary” column.

rstrip → used to remove trailing whitespaces/character mentioned.

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

---

References:

https://pandas.pydata.org/pandas-docs/version/0.22/api.html#string-handling

Thanks for reading!

---

If you like to read more of my tutorials on Python and Data Science,
follow me on medium, Twitter

Become a Medium Member by Clicking here: https://indhumathychelliah.medium.com/membership

)
s1.head()

Example 3: Let’s strip the trailing ‘$’ symbol from the “Salary” column.

rstrip → used to remove trailing whitespaces/character mentioned.

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

---

References:

https://pandas.pydata.org/pandas-docs/version/0.22/api.html#string-handling

Thanks for reading!

---

If you like to read more of my tutorials on Python and Data Science,
follow me on medium, Twitter

Become a Medium Member by Clicking here: https://indhumathychelliah.medium.com/membership

)
s1.head()

Example 2: Let’s strip leading ‘$’ from the “Salary” column.

lstrip → used to remove leading whitespaces/character mentioned.

Example 3: Let’s strip the trailing ‘$’ symbol from the “Salary” column.

rstrip → used to remove trailing whitespaces/character mentioned.

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

---

References:

https://pandas.pydata.org/pandas-docs/version/0.22/api.html#string-handling

Thanks for reading!

---

If you like to read more of my tutorials on Python and Data Science,
follow me on medium, Twitter

Become a Medium Member by Clicking here: https://indhumathychelliah.medium.com/membership

)
s1.head()

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

---

References:

https://pandas.pydata.org/pandas-docs/version/0.22/api.html#string-handling

Thanks for reading!

---

If you like to read more of my tutorials on Python and Data Science,
follow me on medium, Twitter

Become a Medium Member by Clicking here: https://indhumathychelliah.medium.com/membership

)
s1.head()

Example 2: Let’s strip leading ‘$’ from the “Salary” column.

lstrip → used to remove leading whitespaces/character mentioned.

Example 3: Let’s strip the trailing ‘$’ symbol from the “Salary” column.

rstrip → used to remove trailing whitespaces/character mentioned.

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

s1.head()

Example 3: Let’s strip the trailing ‘$’ symbol from the “Salary” column.

rstrip → used to remove trailing whitespaces/character mentioned.

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

---

References:

https://pandas.pydata.org/pandas-docs/version/0.22/api.html#string-handling

Thanks for reading!

---

If you like to read more of my tutorials on Python and Data Science,
follow me on medium, Twitter

Become a Medium Member by Clicking here: https://indhumathychelliah.medium.com/membership

)
s1.head()

Example 2: Let’s strip leading ‘$’ from the “Salary” column.

lstrip → used to remove leading whitespaces/character mentioned.

Example 3: Let’s strip the trailing ‘$’ symbol from the “Salary” column.

rstrip → used to remove trailing whitespaces/character mentioned.

3. split

If we need to split the column in the dataframe into two columns based on some delimiter string, we can use the str.split() function

Example 1: Splitting “Place” column into “City” and “State” columns based on delimiter string “,”

4. replace

If we need to replace a substring in a column in the pandas dataframe, we can use the str. replace() function.

Example: Replacing “nc” by “North Carolina” in the “State” column

5. startswith,endswith, contains

startswith

str. startswith(“prefix”) → Returns True if the string starts with the mentioned “prefix”.
We can apply this function to a column in pandas dataframe, to filter the rows that start with the mentioned “substring” in a particular column.

Example 1: Filtering rows that startswith “C” in the “Place” column

endswith

str. endswith(“suffix”) → Returns True if the string endswith the mentioned “suffix”.
We can apply this function to a column in pandas dataframe, to filter the rows that end with the mentioned “substring” in a particular column.

Example 2: Filtering rows that endswith “as” in the “Place” column

contains

Example 2: Filtering rows that contain the substring “lotte” in the “Place” column

Thanks for reading!

---

If you like to read more of my tutorials on Python and Data Science,
follow me on medium, Twitter