Working with Limited Data Sets: A Deep Dive into xlim

As data scientists, we often find ourselves working with large datasets that contain valuable information. However, in some cases, it’s necessary to limit the dataset to a specific range or subset of values. In this article, we’ll explore how to achieve this using Python and its popular libraries, Pandas, NumPy, and Matplotlib.

We’ll also delve into the world of data transformations, specifically focusing on the xlim (x-axis limits) feature in Matplotlib. By the end of this article, you’ll understand how to work with limited datasets, perform data transformations, and visualize your results using Python’s powerful libraries.

Understanding xlim

Before we dive into the code, let’s first understand what xlim is all about. In Matplotlib, xlim is a function that allows us to set the x-axis limits for our plots. When you call xlim, you’re specifying a range of values that will be displayed on the x-axis.

In the context of this article, we want to limit our dataset to a specific range of x-values. We’ll use Matplotlib’s xlim feature to achieve this.

Working with Large Datasets

Let’s start by assuming that we have a large dataset stored in a Pandas DataFrame called files_data. Our dataset contains multiple files, each with its own set of data.

for key, value in files_data.items():
    file_short_name = key
    # main = value[1]
    data = pd.DataFrame(value[0])

In this loop, we’re iterating over the files_data dictionary and creating a new Pandas DataFrame for each file. We’ll then perform some transformations on the data.

Data Transformation

Let’s say we want to add a new column called newx to our dataset. This column will contain the transformed x-values that we’ll use later.

data["newx"] = -c*(((data.x*(1/(1+D)))-b)/b)

In this line of code, we’re performing a complex transformation on the x values in our dataset. The constants c, b, and D are defined earlier in the script.

Limited Data Set

Now that we’ve transformed our data, we want to limit it to a specific range of x-values. We’ll use Matplotlib’s xlim feature to achieve this.

w = data[(data.newx < 20000) & (data.newx > 8000)]

In this line of code, we’re creating a new DataFrame called w. This DataFrame will contain only the rows from our original dataset where the newx values are within the range of 8000 and 20000.

Gaussian Model

After limiting our data set, we’ll use a Gaussian model to fit the transformed data. We’ll define our model using Matplotlib’s offset module.

pars = offset.make_params(c=np.median(dfy))
pars += peak.guess(dfy, x= dfy, amplitude=-0.5)
result = model.fit(dfy, pars, dfx)

In this block of code, we’re defining our Gaussian model using the offset module. We’ll use the make_params function to create a set of parameters for our model and then fit it to our data.

Combining Code

Now that we’ve covered all the individual components, let’s combine them into a single script.

import pandas as pd
import numpy as np
from matplotlib.offset import make_params

files_data = { # Load your dataset here }

for key, value in files_data.items():
    file_short_name = key
    data = pd.DataFrame(value[0])

    if data.shape[1] == 3:
        data.columns = ["x", "y", "yerr"]
    else:
        data.columns = ["x", "y"]

    D = value[1]
    b = 111
    c = 222

    data["newx"] = -c*(((data.x*(1/(1+D)))-b)/b)
    data["newy"] = (data.y-data.y.min())/(data.y.max()-data.y.min())

    w = data[(data.newx < 20000) & (data.newx > 8000)]
    dfx = w["newx"]
    dfy = w["newy"]

    pars = make_params(c=np.median(dfy))
    pars += peak.guess(dfy, x= dfy, amplitude=-0.5)
    result = model.fit(dfy, pars, dfx)

# Visualize the results
import matplotlib.pyplot as plt

plt.plot(dfx, dfy)
plt.xlim(8000, 20000) # Set the x-axis limits
plt.show()

In this script, we’re loading our dataset, performing data transformations, fitting a Gaussian model to our data, and visualizing the results.

Conclusion

Working with limited datasets can be an intimidating task, but with Python’s powerful libraries like Pandas, NumPy, and Matplotlib, it’s easier than ever. In this article, we covered how to limit your dataset using Matplotlib’s xlim feature and perform data transformations using Pandas. We also used a Gaussian model to fit our data and visualize the results.

By following these steps, you’ll be able to work with limited datasets like a pro!

Last modified on 2023-07-10