Construct dataset

import pandas as pd
df ={'full name':[' Classmate Huang','Huang Zhizun','Huang Laoxie ','Da Mei Chen','Sun Shangxiang'],
     'English name':['Huang tong_xue','huang zhi_zun','Huang Lao_xie','Chen Da_mei','sun shang_xiang'],
     'Gender':['male','women','men','female','male'],
     'ID':['463895200003128433','429475199912122345','420934199110102311','431085200005230122','420953199509082345'],
     'height':['mid:175_good','low:165_bad','low:159_bad','high:180_verygood','low:172_bad'],
     'Home address':['Guangshui, Hubei','Xinyang, Henan','Guangxi Guilin','Hubei Xiaogan','Guangzhou, Guangdong'],
     'Telephone number':['13434813546','19748672895','16728613064','14561586431','19384683910'],
     'income':['1.1 ten thousand','8.5 thousand','0.9 ten thousand','6.5 thousand','2.0 ten thousand']}
df = pd.DataFrame(df)
df

design sketch:

 

1. cat function

This function is mainly used for string splicing;

df["full name"].str.cat(df["Home address"],sep='-'*3)

design sketch:

 

2. contains function

This function is mainly used to {judge whether a string contains a given character;

df["Home address"].str.contains("wide")

design sketch:

 

 3. Startswitch and endswitch functions

This function is mainly used to} determine whether a string is represented by Beginning / end;

# "Huang Wei" in the first line begins with a space
df["full name"].str.startswith("yellow") 
df["English name"].str.endswith("e")

design sketch:

 

 4. count function

This function is mainly used to} calculate the number of times a given character appears in the string;

df["Telephone number"].str.count("3")

design sketch:

 

 5. get function

This function is mainly used to {get the string at the specified position;

df["full name"].str.get(-1)
df["height"].str.split(":")
df["height"].str.split(":").str.get(0)

design sketch:

 

 6. len function

This function is mainly used to {calculate the length of the string;

df["Gender"].str.len()

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 7. upper and lower functions

This function is mainly used for {English case conversion;

df["English name"].str.upper()
df["English name"].str.lower()

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 8. pad+side parameter / center function

This function is mainly used to {add a given character to the left, right or left and right sides of a string;

df["Home address"].str.pad(10,fillchar="*")      # Equivalent to ljust()
df["Home address"].str.pad(10,side="right",fillchar="*")    # Equivalent to rjust()
df["Home address"].str.center(10,fillchar="*")

design sketch:

 

 9. repeat function

This function is mainly used to {repeat the string several times;

df["Gender"].str.repeat(3)

design sketch:

 

 10.  slice_replace function

This function is mainly used to} use the given string to replace the characters at the specified position;

df["Telephone number"].str.slice_replace(4,8,"*"*4)

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 11. replace function

This function is mainly used to} replace the character at the specified position with the given string;

df["height"].str.replace(":","-")

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This function also} accepts a regular expression to replace the character at the specified position with the given string.

df["income"].str.replace("\d+\.\d+","regular")

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 12. split method + expand parameter

This function is mainly used to} expand a column into several columns;

# Common usage
df["height"].str.split(":")
# split method with expand parameter
df[["Height description","final height"]] = df["height"].str.split(":",expand=True)
df
# split method with join method
df["height"].str.split(":").str.join("?"*5)

design sketch:

 

 

13. strip, rstrip and lstrip functions

This function is mainly used to {remove blank characters and line breaks;

df["full name"].str.len()
df["full name"] = df["full name"].str.strip()
df["full name"].str.len()

design sketch:

 

 14. findall function

This function is mainly used to} use regular expressions to match strings and return a list of search results;

df["height"]
df["height"].str.findall("[a-zA-Z]+")

design sketch:

 

 15. extract, extractall functions

This function is mainly used to} accept regular expressions and extract matching strings (be sure to add parentheses);

df["height"].str.extract("([a-zA-Z]+)")
# Extract the composite index from extractall
df["height"].str.extractall("([a-zA-Z]+)")
# extract with expand parameter
df["height"].str.extract("([a-zA-Z]+).*?([a-zA-Z]+)",expand=True)

design sketch: