Working with Dates in Pandas DataFrames

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Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to handle dates efficiently. In this article, we’ll explore how to pick out dates from a column in a pandas DataFrame and move them over to a new column.

Understanding Date Formats

Before we dive into the code, let’s take a closer look at date formats. In the example provided, the date format is mm/dd/yy. However, pandas supports various date formats, including:

• YYYY-MM-DD
• dd/mm/YYYY
• mm/dd/yyyy

To handle dates in pandas, we’ll use the date object, which is a part of the datetime module. We can create a date object from a string using the pd.to_datetime() function.

Converting Date Strings to date Objects

Let’s start by creating a sample DataFrame with a date column:

import pandas as pd

# Create a sample DataFrame
data = {
    'col1': ['12/30/19', 'apple', 'banana', 'peach', 'grapes', 'berries', '1/2/20', 'chocolate', 'vanilla', 'strawberry', '1/5/20', 'cookie', 'cream'],
}
df = pd.DataFrame(data)

# Print the DataFrame
print(df)

Output:

To convert the date strings to date objects, we can use the pd.to_datetime() function:

# Convert the date column to datetime objects
df['col1'] = pd.to_datetime(df['col1'], format='%m/%d/%y')

Output:

Now that we have the date column as datetime objects, we can use various methods to manipulate them.

Filtering Date Columns

To filter out non-date values from a column, we can use the str.contains() method:

# Filter the rows where col1 contains a date string
date_rows = df.loc[df['col1'].str.contains('\d+/\d+/\d+')]

print(date_rows)

Output:

In this example, we’re using the \d+/\d+/\d+ pattern to match date strings in the format mm/dd/yy. The resulting DataFrame contains only rows where the value in col1 is a date string.

Forward Filling Date Columns

To forward fill date columns, we can use the ffill() method:

# Forward fill the date column
df['col2'] = df.loc[df['col1'].str.contains('\d+/\d+/\d+'), 'col1'].ffill()

print(df)

Output:

In this example, we’re using the loc method to filter the rows where col1 contains a date string. We then use the ffill() method to forward fill the values in the filtered DataFrame.

Combining Filtering and Forward Filling

To combine filtering and forward filling, we can chain the two operations:

# Filter out non-date values from col1, then forward fill
df['col2'] = df.loc[df['col1'].str.contains('\d+/\d+/\d+')].groupby('col1')['col1'].ffill()

print(df)

Output:

In this example, we’re using the groupby method to group the rows by the date values in col1, and then applying the ffill() method to forward fill the values.

Conclusion

Working with dates in pandas can seem daunting at first, but once you understand how to manipulate them, it’s relatively straightforward. By filtering out non-date values from a column using the str.contains() method and then forward filling the date column using the ffill() method, you can efficiently extract and manipulate date data.

In this article, we’ve covered how to:

• Convert date strings to datetime objects
• Filter out non-date values from a column
• Forward fill date columns

These techniques are essential for working with date data in pandas.

Last modified on 2023-07-16