Conditional Statements and String Comparison in Python

Introduction

In this article, we will explore conditional statements in Python, focusing on string comparison. We will discuss various ways to achieve different conditions and output results. This article is a response to a Stack Overflow question where the user was experiencing issues with their code.

Conditional Statements

In Python, conditional statements are used to execute different blocks of code based on certain conditions. The most common type of conditional statement is the if statement.

If Statement

The if statement is used to check if a condition is true or false and then execute a block of code if it’s true.

# If statement example
x = 5
if x > 10:
    print("x is greater than 10")
else:
    print("x is less than or equal to 10")

In this example, x is compared to the value 10. If x is greater than 10, it prints “x is greater than 10”. Otherwise, it prints “x is less than or equal to 10”.

Conditional Expressions

Python also supports conditional expressions, which provide a more concise way of writing simple if-else statements.

# Conditional expression example
x = 5
result = x > 10 and "Greater than" or "Less than or equal"
print(result)

In this example, x is compared to the value 10. If x is greater than 10, it prints “Greater than”. Otherwise, it prints “Less than or equal”.

String Comparison

When comparing strings in Python, you need to be aware of the different methods and considerations. Here are some key points:

• Comparison operators: The comparison operators for strings in Python are == (equal), != (not equal), > (greater than), < (less than), >= (greater than or equal to), and <= (less than or equal to).
  • For example: "apple" == "banana" returns False, while "apple" != "banana" returns True.
• Case sensitivity: String comparison in Python is case-sensitive. This means that “Apple” and “apple” are considered as two different strings.
  • For example: "Apple" == "apple" returns False.
• Leading/trailing whitespace: When comparing strings, leading or trailing whitespace can affect the result. For example, " apple " and "apple" would be compared incorrectly if we were to use a simple string comparison.

Example with strings

# Strings example
s1 = "apple"
s2 = "banana"

if s1 == s2:
    print("s1 is equal to s2")
elif s1 > s2:
    print("s1 is greater than s2")
else:
    print("s1 is less than or equal to s2")

# Leading/trailing whitespace example
s3 = "   apple   "
if s3 == s2:
    print("s3 is equal to s2")
elif s3 > s2:
    print("s3 is greater than s2")
else:
    print("s3 is less than or equal to s2")

Using str.strip() method

When comparing strings with leading/trailing whitespace, you can use the strip() method to remove any whitespace from both sides of the string.

# Using strip() example
s4 = "apple"
if s4.strip() == s2:
    print("s4 is equal to s2")
elif s4.strip() > s2:
    print("s4 is greater than s2")
else:
    print("s4 is less than or equal to s2")

Using str.lower() method

When comparing strings with different cases, you can use the lower() method to convert both strings to lowercase before comparison.

# Using lower() example
if s1.lower() == s2:
    print("s1 is equal to s2")
elif s1.lower() > s2:
    print("s1 is greater than s2")
else:
    print("s1 is less than or equal to s2")

Dataframe Manipulation with Pandas

In this section, we’ll focus on using the Pandas library for data manipulation and conditional statements.

Creating a DataFrame

# Import pandas library
import pandas as pd

# Create a DataFrame
data = {
    "FCH_REC": ["2022-01-01", "2022-02-02", "2022-03-03"],
    "LL": [201, 400, 500],
    "DEC": ["RC", "RCLA", "TEST"]
}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

Conditional Statements in DataFrames

When working with DataFrames, you can use Pandas’ built-in functions to perform conditional statements.

# Using loc[] for conditional statement example
# First create a new column "FCH_REC" and fill it with None if the conditions are met.
df.loc[(df['LL'] == 201) & (df['DEC'] == 'RC'), "FCH_REC"] = None

# Display the updated DataFrame
print(df)

The above example creates a new column in the DataFrame named FCH_REC and fills it with None if the condition (df['LL'] == 201) & (df['DEC'] == 'RC') is met.

Using Apply Function

You can use Pandas’ apply function to achieve more complex conditional statements. Here’s an example:

# Using apply for conditional statement example
def func(row):
    if row["LL"] in ["RC", "RCLA"]:
        return "None"
    else:
        return row["FCH_REC"]

df["FCH_REC"] = df.apply(func, axis=1)

# Display the updated DataFrame
print(df)

This example creates a new column named FCH_REC and fills it with "None" if the condition (row["LL"] in ["RC", "RCLA"]) is met.

Looping through DataFrames

When you have a large number of rows or columns to manipulate, looping can be an efficient way to achieve your goals. Here’s how:

# Using for loop to set values example
for i in range(df.shape[0]):
    if df.loc[i,"DEC"] in ["RC", "RCLA"]:
        df.loc[i,"FCH_REC"] = "NONE"

# Display the updated DataFrame
print(df)

In this case, we are using a for loop to iterate over each row of the DataFrame and set the value of the FCH_REC column to "NONE" if the condition (df.loc[i,"DEC"]) in ["RC", "RCLA"] is met.

Troubleshooting

Based on your provided Stack Overflow question, it seems like you’re using Python’s Pandas library for data manipulation. Here are a few common mistakes and potential solutions that might help resolve the issue:

• Mistake: Incorrect use of conditional statements or leading/trailing whitespace.

  • Solution: Double-check how you’re handling strings with leading/trailing whitespace by removing whitespace using str.strip() method, or convert to lowercase using lower().

• Mistake: Using an incorrect data type for your DataFrame columns.

  • Solution: Ensure that the correct data type is used for each column in your DataFrame. In this case, you should use string type (e.g., "str") instead of integer type (“int”).

Conclusion

Conditional statements and string comparison are fundamental concepts in Python programming. When working with Pandas DataFrames, it’s also crucial to understand how to manipulate data using various functions like loc[] and apply().

In this article, we have explored different ways to perform conditional statements and string comparisons, including leading/trailing whitespace considerations and looping through large datasets.

Last modified on 2024-08-11