Method Chaining

Method chaining is a programming pattern in Pandas that allows us to apply a sequence of operations to a dataframe in a single line of code.

Example: In the below example, We are performing the operations like dropping nan values, renaming the column, grouping and resetting the index as separate steps. Each operation creates an intermediate dataframe which is modified in the next step. This leads to increased memory usage.

Python3

# Sample data 
data = { 
    'column1': [1, 2, None, 4, 5], 
    'column2': ['A', 'B', 'C', 'D', 'E'] 
} 
# Without method chaining 
df = pd.DataFrame(data) 
df = df.dropna(subset=['column1']) 
df = df.rename(columns={'column2': 'new_column'}) 
df = df.reset_index(drop=True) 
print("DataFrame without method chaining:") 
print(df)

Output:

DataFrame without method chaining:
   column1 new_column
0      1.0          A
1      2.0          B
2      4.0          D
3      5.0          E

Using the method chaining method, each operation is applied directly to the dataframe. This reduces the memory usage and enhances the conciseness. To ensure correct method chaining use parenthesis.

Python3

# With method chaining 
df = (pd.DataFrame(data) 
      .dropna(subset=['column1']) 
      .rename(columns={'column2': 'new_column'}) 
      .reset_index(drop=True)) 
print("\nDataFrame with method chaining:") 
print(df) 

Output:

DataFrame with method chaining:
   column1 new_column
0      1.0          A
1      2.0          B
2      4.0          D
3      5.0          E

10 Python Pandas tips to make data analysis faster

Data analysis using Python’s Pandas library is a powerful process, and its efficiency can be enhanced with specific tricks and techniques. These Python tips will make our code concise, readable, and efficient. The adaptability of Pandas makes it an efficient tool for working with structured data. Whether you are a beginner or an experienced data scientist, mastering these Python tips can help you enhance your efficiency in data analysis tasks.

Pandas tips for Data Analysis

In this article we will explore about What are the various 10 python panads tips to make data analysis faster and that helps us to make our work more easier.

Table of Content

Use Vectorized Operation
Optimize Memory Usage
Method Chaining
Use GroupBy Aggregations
Using describe() and Percentile
Leverage the Power of pd.cut and pd.qcut
Optimize DataFrame Merging
Use isin for Filtering
Profile Code with ydata_profiling
Conclusion

Method Chaining

Python3

Python3

10 Python Pandas tips to make data analysis faster

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