How to Convert Dates in Pandas and SQLAlchemy for Efficient Data Management

Working with Dates in Pandas and SQLAlchemy

As data scientists and developers, we often encounter date-related issues when working with different programming languages and databases. In this article, we’ll explore how to convert a pandas object of type object containing dates in the format “YYYY-MM” into a datetime object using pandas’ to_datetime() function. We’ll also discuss how to insert these datetime objects into a MySQL database using SQLAlchemy.

Understanding Pandas Objects

In pandas, data is stored as objects that can be of various types, including int64, float64, object, and others. The object type is used to store strings or other types of data that cannot be represented by a specific numeric type. When working with dates in pandas, it’s essential to understand the difference between object and datetime64[ns] (a 64-bit integer representing seconds since the Unix epoch).

In our example, we have a pandas DataFrame df with a column named “month” containing dates in the format “YYYY-MM”. However, these dates are stored as strings in the object type:

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'month': ['2016-01', '2017-02', '2018-03']
})

# Print the first few rows of the DataFrame
print(df.head())

Output:

As you can see, the “month” column contains dates in the format “YYYY-MM”.

Converting Dates to datetime Objects

When we try to convert these date strings to datetime objects using pandas’ to_datetime() function, it automatically detects the format of the dates and converts them accordingly:

# Convert the 'month' column to datetime objects
df['month'] = pd.to_datetime(df['month'])

# Print the first few rows of the DataFrame
print(df.head())

Output:

   month
0 2016-01-01
1 2017-02-01
2 2018-03-01

As you can see, the to_datetime() function has successfully converted the date strings to datetime objects.

However, we notice that the dates have additional information (days) which we don’t want. To remove this extra information, we need to extract only the year and month from the datetime object.

Extracting Year and Month

We can use pandas’ dt accessor to access the datetime components of a datetime object:

# Extract the year and month from the datetime objects
df['year'] = df['month'].dt.year
df['month_name'] = df['month'].dt.strftime('%B')

# Print the first few rows of the DataFrame
print(df.head())

Output:

   month  year month_name
0 2016-01  2016     January
1 2017-02  2017     February
2 2018-03  2018     March

As you can see, the year and month_name columns contain only the year and month information, respectively.

Inserting Dates into a MySQL Database using SQLAlchemy

Now that we have extracted the year and month from our datetime objects, we need to insert them into a MySQL database using SQLAlchemy. We’ll use the create_engine() function to establish a connection to our MySQL database:

# Import necessary libraries
from sqlalchemy import create_engine

# Establish a connection to the MySQL database
engine = create_engine('mysql+pymysql://username:password@localhost/dbname')

# Insert the extracted year and month into the database
df[['year', 'month_name']].to_sql('dates', engine, if_exists='replace', index=False)

Note that we’re using the to_sql() function to insert the data into the database. We specify the table name 'dates', the columns to be inserted (year and month_name), and other parameters such as if_exists='replace' (which replaces the existing table if it exists) and index=False (which prevents SQLAlchemy from adding an index column).

Conclusion

In this article, we discussed how to convert a pandas object of type object containing dates in the format “YYYY-MM” into datetime objects using pandas’ to_datetime() function. We also explored how to extract year and month information from these datetime objects using pandas’ dt accessor. Finally, we demonstrated how to insert these extracted values into a MySQL database using SQLAlchemy.

By following this article, you should now have a better understanding of working with dates in pandas and SQLAlchemy, as well as how to convert date strings to datetime objects and extract specific information from them.

Last modified on 2025-01-05