Loading Data into Databases and Files
import pandas as pd
import sqlite3
# Create a sample DataFrame
data = {
"id": [1, 2, 3],
"name": ["Alice", "Bob", "Charlie"],
"score": [85, 92, 78]
}
df = pd.DataFrame(data)
# Connect to SQLite database (or create it)
conn = sqlite3.connect("students.db")
# Write DataFrame to SQLite table named 'students'
df.to_sql("students", conn, if_exists="replace", index=False)
# Close the connection
conn.close()
When loading data into databases or exporting to files, you must consider how your DataFrame's schema maps to the target system. Pay attention to column names and data types—SQLite will infer types from pandas, but mismatches can lead to unexpected results. For file exports, choose the format that best fits your use case: CSV is widely supported and human-readable, while JSON is ideal for nested or hierarchical data. Always check that your data types, encodings, and delimiters align with the requirements of downstream systems or consumers.
import pandas as pd
# Create a sample DataFrame
data = {
"id": [1, 2, 3],
"name": ["Alice", "Bob", "Charlie"],
"score": [85, 92, 78]
}
df = pd.DataFrame(data)
# Export DataFrame to CSV file
df.to_csv("students.csv", index=False)
# Export DataFrame to JSON file
df.to_json("students.json", orient="records", lines=True)
When loading data into databases or exporting to files, you must consider how your DataFrame's schema maps to the target system. Pay attention to column names and data types—SQLite will infer types from pandas, but mismatches can lead to unexpected results. For file exports, choose the format that best fits your use case: CSV is widely supported and human-readable, while JSON is ideal for nested or hierarchical data. Always check that your data types, encodings, and delimiters align with the requirements of downstream systems or consumers.
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import pandas as pd
import sqlite3
# Create a sample DataFrame
data = {
"id": [1, 2, 3],
"name": ["Alice", "Bob", "Charlie"],
"score": [85, 92, 78]
}
df = pd.DataFrame(data)
# Connect to SQLite database (or create it)
conn = sqlite3.connect("students.db")
# Write DataFrame to SQLite table named 'students'
df.to_sql("students", conn, if_exists="replace", index=False)
# Close the connection
conn.close()
When loading data into databases or exporting to files, you must consider how your DataFrame's schema maps to the target system. Pay attention to column names and data types—SQLite will infer types from pandas, but mismatches can lead to unexpected results. For file exports, choose the format that best fits your use case: CSV is widely supported and human-readable, while JSON is ideal for nested or hierarchical data. Always check that your data types, encodings, and delimiters align with the requirements of downstream systems or consumers.
import pandas as pd
# Create a sample DataFrame
data = {
"id": [1, 2, 3],
"name": ["Alice", "Bob", "Charlie"],
"score": [85, 92, 78]
}
df = pd.DataFrame(data)
# Export DataFrame to CSV file
df.to_csv("students.csv", index=False)
# Export DataFrame to JSON file
df.to_json("students.json", orient="records", lines=True)
When loading data into databases or exporting to files, you must consider how your DataFrame's schema maps to the target system. Pay attention to column names and data types—SQLite will infer types from pandas, but mismatches can lead to unexpected results. For file exports, choose the format that best fits your use case: CSV is widely supported and human-readable, while JSON is ideal for nested or hierarchical data. Always check that your data types, encodings, and delimiters align with the requirements of downstream systems or consumers.
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