"Large data" workflows using pandas

Here is an example of a workflow for handling large data using the pandas library:

import pandas as pd

# Read in large data file using the chunksize parameter to read in chunks
# instead of loading the entire file into memory
df_iterator = pd.read_csv("large_data.csv", chunksize=100000)

# Process each chunk of data
for df_chunk in df_iterator:
    # Perform data cleaning and preprocessing on chunk
    df_chunk = df_chunk.dropna()
    df_chunk["column_name"] = df_chunk["column_name"].str.lower()
    # Append processed chunk to a list

# Concatenate all chunks into a single dataframe
final_df = pd.concat(processed_data)

# Perform further analysis or export data
final_df.to_csv("cleaned_large_data.csv", index=False)

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In this example, the large data file is read in using the pd.read_csv() function with the chunksize parameter set to 100000. This reads in the file in chunks of 100000 rows, allowing for the data to be processed in smaller chunks rather than loading the entire file into memory. Each chunk is then cleaned and preprocessed, and the cleaned chunks are appended to a list. Once all chunks have been processed, the list of chunks is concatenated into a single dataframe using pd.concat(). The final dataframe can then be used for further analysis or exported to a new file.