Adventures in Machine Learning

Combining Date and Time Columns with Pandas: A Practical Guide

Combining Date and Time Columns in Pandas

Time and space are two fundamental concepts that we as humans struggle to comprehend. In the world of data analysis and management, it’s no different.

We store data that represents events that occur in space and time in many formats, and sometimes we need to work with this data together. Fortunately, Python’s Pandas library with its DataFrame object and methods make it easier for analysts and data scientists to handle such data-rich questions.

In this article, we will explore how to combine date and time columns to create a new column in a Pandas DataFrame. We will also look at how to convert non-string columns to strings.

We will guide you through the process with easy-to-follow examples and provide valuable tips to make the process as smooth as possible.

Combining Date and Time Columns in Pandas

To illustrate how to combine date and time columns, we will create a sample data frame with columns for date and time. The first thing to do is to import the Pandas library and create the DataFrame.

“`

import pandas as pd

df = pd.DataFrame({‘date’: [‘2022-09-21’, ‘2022-09-21’, ‘2022-09-22’],

‘time’: [’12:30:01′, ’13:45:01′, ’15:21:01′]})

“`

Here, we have created a DataFrame with three rows and two columns, ‘date’ and ‘time.’ The date column includes string values representing dates in YYYY-MM-DD format, while the time column includes string values representing timestamps in HH:MM:SS format. Now, let’s combine these two columns to create a new column that represents a datetime object.

To start with, we will use the `pd.to_datetime()` method with the `format` parameter to parse the input strings, including both the date and time strings together. “`

df[‘datetime’] = pd.to_datetime(df[‘date’] + ‘ ‘ + df[‘time’], format=’%Y-%m-%d %H:%M:%S’)

“`

This new column, ‘datetime’, will include parsed datetime values that represent the date and time together.

“`

print(df)

date time datetime

0 2022-09-21 12:30:01 2022-09-21 12:30:01

1 2022-09-21 13:45:01 2022-09-21 13:45:01

2 2022-09-22 15:21:01 2022-09-22 15:21:01

“`

Here, we can see that the new column ‘datetime’ includes the combined date and time values in a pandas datetime object.

Converting Non-String Columns to Strings

When working with a Pandas DataFrame, we should also know how to convert non-string columns to strings. To illustrate how to do this, let’s consider a simple example.

“`

import pandas as pd

df = pd.DataFrame({‘number’: [1, 2, 3, 4],

‘float’: [1.2, 2.3, 3.4, 4.5],

‘bool’: [True, False, False, True],

‘date’: [‘2022-09-21’, ‘2022-09-21’, ‘2022-09-22’, ‘2022-09-22’],

‘text’: [‘Hello’, ‘World’, ‘Python’, ‘Data Science’]})

“`

In this DataFrame, we have columns with different data types, such as integers, floats, booleans, dates, and text. “`

print(df.dtypes)

number int64

float float64

bool bool

date object

text object

dtype: object

“`

We can see that the ‘number,’ ‘float,’ and ‘bool’ columns have numerical or boolean data types, and both ‘date’ and ‘text’ columns have the ‘object’ datatype. We can use the `astype(str)` method to convert the non-string columns to strings.

“`

df[‘number’] = df[‘number’].astype(str)

df[‘float’] = df[‘float’].astype(str)

df[‘bool’] = df[‘bool’].astype(str)

df[‘date’] = df[‘date’].astype(str)

“`

Now, let’s check the data types of each column again. “`

print(df.dtypes)

number object

float object

bool object

date object

text object

dtype: object

“`

We can see that all of the non-string columns have been converted to string columns.

Conclusion

In this article, we have explored how to combine date and time columns to create a new column in a Pandas DataFrame. We have also walked you through the process of converting non-string columns to strings.

These are handy techniques that can be used every day when working with data in Python. By following our straightforward examples and tips, you can efficiently handle your data needs with Pandas and Python.

In this article, we have explored how to combine date and time columns in a Pandas DataFrame, as well as the process of converting non-string columns to strings. These are essential skills for data analysts and data scientists working with real-world data.

By following the examples and tips we have provided, you can efficiently and accurately handle data that requires date and time information. The takeaway from this article is that with Pandas, Python users have powerful tools at their disposal, which can simplify their work, and increase their productivity.

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