Text Data In Python Cheat Sheet

Example data used throughout this cheat sheet

Throughout this cheat sheet, we’ll be using two pandas series named suits and rock_paper_scissors.

import pandas as pd

suits = pd.Series(["clubs", "Diamonds", "hearts", "Spades"])
rock_paper_scissors = pd.Series(["rock ", " paper", "scissors"])提供：

String lengths and substrings

# Get the number of characters with .str.len()
suits.str.len() # Returns 5 8 6 6

# Get substrings by position with .str[]
suits.str[2:5] # Returns "ubs" "amo" "art" "ade"

# Get substrings by negative position with .str[]
suits.str[:-3] # "cl" "Diamo" "hea" "Spa

# Remove whitespace from the start/end with .str.strip()
rock_paper_scissors.str.strip() # "rock" "paper" "scissors"

# Pad strings to a given length with .str.pad()
suits.str.pad(8, fillchar="_") # "___clubs" "Diamonds" "__hearts" "__Spades"提供：

Changing case

# Convert to lowercase with .str.lower()
suits.str.lower() # "clubs" "diamonds" "hearts" "spades"

# Convert to uppercase with .str.upper()
suits.str.upper() # "CLUBS" "DIAMONDS" "HEARTS" "SPADES"

# Convert to title case with .str.title()
pd.Series("hello, world!").str.title() # "Hello, World!"

# Convert to sentence case with .str.capitalize()
pd.Series("hello, world!").str.capitalize() # "Hello, world!"提供：

Formatting settings

# Generate an example DataFramed named df
df = pd.DataFrame({"x": [0.123, 4.567, 8.901]})
#    x
#  0 0.123
#  1 4.567
#  2 8.901

# Visualize and format table output
df.style.format(precision = 1)提供：

Splitting strings

# Split strings into list of characters with .str.split(pat="")
suits.str.split(pat="")

# [, "c" "l" "u" "b" "s", ]
# [, "D" "i" "a" "m" "o" "n" "d" "s", ]
# [, "h" "e" "a" "r" "t" "s", ]
# [, "S" "p" "a" "d" "e" "s", ]

# Split strings by a separator with .str.split()
suits.str.split(pat = "a")

# ["clubs"]
# ["Di", "monds"]
# ["he", "rts"]
# ["Sp", "des"]

# Split strings and return DataFrame with .str.split(expand=True)
suits.str.split(pat = "a", expand=True)

#        0      1
# 0  clubs   None
# 1     Di  monds
# 2     he    rts
# 3     Sp    des提供：

Joining or concatenating strings

# Combine two strings with +
suits + "5" # "clubs5" "Diamonds5" "hearts5" "Spades5"

# Collapse character vector to string with .str.cat()
suits.str.cat(sep=", ") # "clubs, Diamonds, hearts, Spades"

# Duplicate and concatenate strings with *
suits * 2 # "clubsclubs" "DiamondsDiamonds" "heartshearts" "SpadesSpades"提供：

Detecting Matches

# Detect if a regex pattern is present in strings with .str.contains()
suits.str.contains("[ae]") # False True True True

# Count the number of matches with .str.count()
suits.str.count("[ae]") # 0 1 2 2

# Locate the position of substrings with str.find()
suits.str.find("e") # -1 -1 1 4提供：

Extracting matches

# Extract matches from strings with str.findall()
suits.str.findall(".[ae]") # [] ["ia"] ["he"[ ["pa", "de"]

# Extract capture groups with .str.extractall()
suits.str.extractall("([ae])(.)")
#            0 1
#   match
# 1 0        a m
# 2 0        e a
# 3 0        a d
#   1        e s

# Get subset of strings that match with x[x.str.contains()]
suits[suits.str.contains("d")] # "Diamonds" "Spades"提供：

Replacing matches

# Replace a regex match with another string with .str.replace()
suits.str.replace("a", "4") # "clubs" "Di4monds" "he4rts" "Sp4des"

# Remove a suffix with .str.removesuffix()
suits.str.removesuffix # "club" "Diamond" "heart" "Spade"

# Replace a substring with .str.slice_replace()
rhymes = pd.Series(["vein", "gain", "deign"])
rhymes.str.slice_replace(0, 1, "r") # "rein" "rain" "reign"
提供：