How to add metadata to a DataFrame or Series with Pandas in Python?

One of the key features of Pandas is the ability to work with metadata, which can provide additional information about the data that is present in a DataFrame or Series. Pandas is a powerful and widely used library in Python which is used for data manipulation and analysis. In this article, we will explore how to add metadata to a Dataframe or a Series with Pandas in Python.

What is metadata in Pandas?

Metadata is the information about the data in a DataFrame or Series. It can include information about the data types of the columns, the units of measurement, or any other important and relevant information that provides context about the data that is provided. Metadata can be added to a DataFrame or Series using Pandas.

Why is metadata important in data analysis?

Metadata is important in data analysis because it provides context and insights about the data. Without metadata, it can be difficult to understand the data and make meaningful conclusions from the data. For example, metadata can help you understand the units of measurement, which can help you make accurate comparisons and calculations. Metadata can also help you understand the data type of columns, which can help us to select the appropriate data analysis tools.

How to add metadata to a dataframe or series with pandas?

Below are steps to add metadata to an dataframe or series −

Apply metadata to the dataframe or series

Pandas provides an attribute called attrs for adding metadata to the dataframe or series. This attribute is an object that is like a dictionary that can be used to store arbitrary metadata. If you want to add metadata to a Dtaframe or series, simply access the attrs attribute and after that set the desired metadata attributes.

In our program, we will be adding a description, a scale factor, and an offset to the dataframe.

Applying Scale and offset to our dataframe

In the next step, we will apply scale and offset to our dataframe. We can do the same by multiplying the DataFrame by the scale factor and after that adding the offset. We can then save the metadata and scaled DataFrame so that we can use it later.

Saving the metadata and Dataframe to an HDFS file

Pandas provides the HDFStore class for working with files that are in HDF5 format. HDF5 is data in a hierarchical format and supports retrieval of large datasets and efficient storage. The HDFStore class provides a convenient way to save and load Dataframe and Series to and from HDF5 files.

To save the metadata and DataFrame to an HDF5 file, we can use put() method that is present inside the HDFStore class. We then specify the format as ‘table’ and omit the metadata argument.

Example

import pandas as pd
import numpy as np

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Add metadata to the DataFrame
df.attrs['description'] = 'Example DataFrame'
df.attrs['scale'] = 0.1
df.attrs['offset'] = 0.5

# Apply scale and offset to the DataFrame
df_scaled = (df * df.attrs['scale']) + df.attrs['offset']

# Save the metadata to an HDF5 file
with pd.HDFStore('example1.h5') as store:
   store.put('data', df_scaled, format='table')
   store.get_storer('data').attrs.metadata = df.attrs

# Read the metadata and DataFrame from the HDF5 file
with pd.HDFStore('example1.h5') as store:
   metadata = store.get_storer('data').attrs.metadata
   df_read = store.get('data')

# Retrieve the scale and offset from the metadata
scale = metadata['scale']
offset = metadata['offset']

# Apply scale and offset to the DataFrame
df_unscaled = (df_read - offset) / scale

# Print the unscaled DataFrame
print(df_unscaled)

Output

     A    B
0  1.0  4.0
1  2.0  5.0
2  3.0  6.0

In the above program, we first created a dataframe df with the following columns A and B. We then added metadata to the dataframe using the attrs attribute and after that, we set the ‘description’, ‘offset’, and ‘scale’ attributes to their respective values.

In the next step, we created a new dataframe df_scaled by applying the scale and the offset to the original dataframe df. We have done the following by multiplying the dataframe by the scale factor and after that, adding the offset to the following.

We then save the metadata and the scaled dataframe to an HDF5 file named example1.h5 using the put() method of the HDFStore class. We specified the format as a ‘table’ and omit the metadata argument. Instead, we set the metadata as an attribute of the HAF5 file using the metadata attribute of the storer object which is returned by get_storer(‘data’) function.

In the next part, read the metadata and the dataframe from the HDF5 file which is named ‘example1.h5’, we use another ‘with’ statement to open the file in read mode using the r parameter. We retrived the metadata by accessing the metadata attribute of the storer object which is returned by get_storer(‘data’) function and we retrieved the dataframe using the get() method of the HDFStore class.

In the final step, we retrieved the scale and the offset from the metadata and then we apply them to the dataframe to obtain the unscaled dataframe. We print the unscaled dataframe to make sure that it has been correctly unscaled.

Conclusion

In conclusion, adding metadata to a Series or a dataframe with Pandas in Python can provide additional context and annotations to our data, making it more informative and useful. We have used attrs attribute of the Dataframe or the Series, we have easily added metadata such as a scale factor, a description, and an offset to our dataframe.