Understanding the Problem: Why append is Slow
The append method, while seemingly straightforward, suffers from performance issues. Each call to append creates a new DataFrame, copying the existing data. This becomes computationally expensive with repeated appends, especially when dealing with substantial datasets.
import pandas as pd
import time
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})
df3 = pd.DataFrame({'A': [9, 10], 'B': [11, 12]})
start_time = time.time()
result_append = df1.append(df2, ignore_index=True).append(df3, ignore_index=True)
end_time = time.time()
print(f"Time taken using append: {end_time - start_time:.4f} seconds")
print(result_append)The above code demonstrates the append method. Notice how we use ignore_index=True to reset the index after each append. While functional, this method is slow for numerous appends.
Superior Alternatives: concat for Efficiency
The pd.concat function offers a significantly more efficient solution. It concatenates DataFrames along a particular axis (0 for rows, 1 for columns). Crucially, concat performs the operation in a more optimized manner, reducing the overhead associated with repeated copying.
import pandas as pd
import time
start_time = time.time()
result_concat = pd.concat([df1, df2, df3], ignore_index=True)
end_time = time.time()
print(f"Time taken using concat: {end_time - start_time:.4f} seconds")
print(result_concat)Observe the marked improvement in speed when using concat. This is the recommended approach for appending multiple DataFrames.
Handling Lists of DataFrames
If you have a list of DataFrames, concat effortlessly handles this scenario.
import pandas as pd
import time
dfs = [df1, df2, df3] #list of dataframes
start_time = time.time()
result_concat_list = pd.concat(dfs, ignore_index=True)
end_time = time.time()
print(f"Time taken using concat with list: {end_time - start_time:.4f} seconds")
print(result_concat_list)Iterative Append: A Less Efficient but Flexible Approach
While less efficient than concat, an iterative approach using a loop and concat offers flexibility when appending DataFrames dynamically or conditionally.
import pandas as pd
import time
dfs = [df1, df2, df3]
result_iterative = pd.DataFrame()
start_time = time.time()
for df in dfs:
result_iterative = pd.concat([result_iterative,df], ignore_index=True)
end_time = time.time()
print(f"Time taken using iterative concat: {end_time - start_time:.4f} seconds")
print(result_iterative)This method shows how to build a DataFrame iteratively, although it’s less efficient than directly using concat on a list.
Choosing the Right Method
For optimal performance, always prioritize pd.concat when appending multiple DataFrames. The iterative approach is useful in scenarios requiring more dynamic control over the appending process. Avoid using append in loops for large datasets due to its significant performance drawbacks. Remember to use ignore_index=True to maintain a continuous index after concatenation.