From 33febf4d5ae9cc85b290be6f7bcf959e009a6c9f Mon Sep 17 00:00:00 2001
From: "deepsource-autofix[bot]"
 <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Date: Fri, 10 May 2024 12:41:55 +0000
Subject: [PATCH] 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

This commit fixes the style issues introduced in 6972938 according to the output
from 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.

Details: None
---
 data_analytics/data_analysis.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/data_analytics/data_analysis.py b/data_analytics/data_analysis.py
index 20bc30414..88523a5c6 100644
--- a/data_analytics/data_analysis.py
+++ b/data_analytics/data_analysis.py
@@ -2,6 +2,7 @@
 import pandas as pd
 from sklearn.cluster import KMeans
 
+
 class DataAnalysis:
     def __init__(self, data):
         self.data = data
@@ -9,16 +10,18 @@ def __init__(self, data):
     def analyze_data(self):
         # Perform data cleaning and preprocessing
         self.data = self.data.dropna()
-        self.data = pd.get_dummies(self.data, columns=['transaction_type'])
+        self.data = pd.get_dummies(self.data, columns=["transaction_type"])
 
         # Perform data analysis
-        kmeans = KMeans(n_clusters=3, random_state=0).fit(self.data[['amount', 'frequency']])
-        self.data['cluster'] = kmeans.labels_
+        kmeans = KMeans(n_clusters=3, random_state=0).fit(
+            self.data[["amount", "frequency"]]
+        )
+        self.data["cluster"] = kmeans.labels_
 
         # Perform statistical analysis
         summary_stats = self.data.describe()
-        summary_stats.loc['count'] = len(self.data)
-        summary_stats.loc['mean'] = np.mean(self.data)
-        summary_stats.loc['std'] = np.std(self.data)
+        summary_stats.loc["count"] = len(self.data)
+        summary_stats.loc["mean"] = np.mean(self.data)
+        summary_stats.loc["std"] = np.std(self.data)
 
         return summary_stats