Combining Timestamp Columns and Filling Missing Values in Read CSV with Pandas

In this article, we will explore how to combine the Date and Time columns of a Pandas DataFrame into a single timestamp column, convert it to seconds since January 1, 1900, and fill missing values using the fillna method.

Introduction

When working with time-series data in Pandas, it’s often necessary to combine multiple columns into a single timestamp column. In this case, we’re dealing with a CSV file that contains date and time information along with some numerical data. The goal is to create a new column that represents the timestamp in seconds since January 1, 1900.

Reading the CSV File

To start, we need to read the CSV file into a Pandas DataFrame using pd.read_csv. We’ll also specify the parse_dates parameter to indicate which columns should be parsed as dates.

import pandas as pd
from io import StringIO
data = StringIO("""\
Date,Time,x1,x2,x3,x4,x5
3/7/2012,11:09:22,13.5,2.3,0.4,7.3,6.4
,,12.6,3.4,9.0,3.0,7.0
,,3.6,4.4,8.0,6.0,5.0
,,10.6,3.5,1.0,3.0,8.0
3/7/2012,11:09:23,10.5,23.2,0.3,7.8,4.4
,,11.6,13.4,19.0,13.0,17.0
""")
df = pd.read_csv(data, parse_dates=['Date']).fillna(method='ffill')

Creating the Timestamp Column

To create a single timestamp column, we can use the apply method to apply a function to each row of the DataFrame. This function takes the date and time values from the Date and Time columns, combines them into a single string in the format ‘YYYY-MM-DD HH:MM:SS’, and then converts this string into a datetime object using datetime.datetime.strptime. We can then subtract the reference date (January 1, 1900) from each datetime object to get the number of seconds since January 1, 1900.

Here’s an example code snippet that demonstrates how to create the timestamp column:

def dealwithdates(row):
    datestring = row['Date'].strftime('%Y-%m-%d')
    dtstring = '{} {}'.format(datestring, row['Time'])
    date = datetime.datetime.strptime(dtstring, '%Y-%m-%d %H:%M:%S')

    refdate = datetime.datetime(1900, 1, 1)
    return (date - refdate).total_seconds()

df['ordinal'] = df.apply(dealwithdates, axis=1)
print(df)

Converting to Seconds Since January 1, 1900

Once we have the timestamp column created, we can convert it to seconds since January 1, 1900 using the astype method.

df['ordinal'] = df['ordinal'].astype(int)

Filling Missing Values

When working with missing values in Pandas, there are several strategies that you can use to fill them. One common approach is to use the fillna method, which replaces missing values with a specified value (e.g., 0 or a specific number). However, when dealing with datetime objects and timestamp columns, we need to be careful to avoid filling in missing values with NaN (not a number) values.

In this case, since we’re working with seconds since January 1, 1900, we can simply fill in missing values by propagating the previous value up to that point using the ffill method.

df.fillna(method='ffill', axis=0, inplace=True)

Conclusion

In this article, we’ve explored how to combine the Date and Time columns of a Pandas DataFrame into a single timestamp column, convert it to seconds since January 1, 1900, and fill missing values using the fillna method. We’ve also discussed some common strategies for working with missing values in Pandas, including propagating previous values up to that point.

By following these steps, you should be able to create a clean and accurate timestamp column in your DataFrame, which can help you analyze and visualize your data more effectively.

Last modified on 2024-01-27