Optimize DataFrame Merging
Merge function in Pandas is used to combine two or more DataFrames based on a common column or index. While merging, the dataframes can be optimized by specifying on and how parameters to improve the performance. Let’s discuss with an example:
1.on Parameter:
The on parameter specifies the column or index on which the merging should occur. If the columns to be merged, have the same name in both DataFrames, we can use this parameter to specify the common column explicitly.
Example: In this example, the on=’ID’ parameter explicitly specifies that the merging should occur based on the ‘ID’ column.
Python3
import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'ID': [1, 2, 3], 'Value1': ['A', 'B', 'C']}) df2 = pd.DataFrame({'ID': [1, 2, 3], 'Value2': ['X', 'Y', 'Z']}) # Explicitly specifying the 'on' parameter merged_df = pd.merge(df1, df2, on='ID') print(merged_df) |
Output:
ID Value1 Value2
0 1 A X
1 2 B Y
2 3 C Z
2. how Parameter:
The how parameter determines the type of merge to be performed. ‘left’, ‘right’, ‘outer’, and ‘inner’ are some common options. We can specify this term explicitly to perform the desired type of merging.
Example:
Python3
import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'ID': [1, 2, 3], 'Value1': ['A', 'B', 'C']}) df2 = pd.DataFrame({'ID': [2, 3, 4], 'Value2': ['X', 'Y', 'Z']}) # Explicitly specifying the 'how' parameter merged_df_inner = pd.merge(df1, df2, on='ID', how='inner') merged_df_outer = pd.merge(df1, df2, on='ID', how='outer') print("Inner Merge:") print(merged_df_inner) print("\nOuter Merge:") print(merged_df_outer) |
Output: In the ‘inner’ merge, only the common IDs present in both DataFrames are retained. In the ‘outer’ merge, all IDs from both DataFrames are retained, filling in missing values with NaN when necessary.
Inner Merge:
ID Value1 Value2
0 2 B X
1 3 C Y
Outer Merge:
ID Value1 Value2
0 1 A NaN
1 2 B X
2 3 C Y
3 4 NaN Z
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
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