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style: format code with Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS, StandardRB, swift-format and Yapf #16

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merged 1 commit into from
May 11, 2024
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style: format code with Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS, StandardRB, swift-format and Yapf #16

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16 changes: 9 additions & 7 deletions models/data_processing/data_analytics.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,12 @@
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

# Define a class for data analysis and visualization


class DataAnalytics:
def __init__(self, data_path):
# Load data from file
Expand All @@ -17,7 +19,7 @@ def summary_statistics(self):
def correlation_matrix(self):
# Compute the correlation matrix for the data
corr = self.data.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.show()

def scatter_plot(self, x_column, y_column):
Expand All @@ -29,21 +31,21 @@ def scatter_plot(self, x_column, y_column):

def bar_plot(self, column):
# Create a bar plot for a column of the data
self.data[column].value_counts().plot(kind='bar')
self.data[column].value_counts().plot(kind="bar")
plt.xlabel(column)
plt.ylabel('Count')
plt.ylabel("Count")
plt.show()

def line_plot(self, column):
# Create a line plot for a column of the data
plt.plot(self.data[column])
plt.xlabel('Index')
plt.xlabel("Index")
plt.ylabel(column)
plt.show()

def histogram_plot(self, column):
# Create a histogram plot for a column of the data
plt.hist(self.data[column])
plt.xlabel(column)
plt.ylabel('Count')
plt.ylabel("Count")
plt.show()
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