Upload tonal_159.py

tonal_159.py

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+# -*- coding: utf-8 -*-
+"""tonal.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1d2iQuX1rG4rDuN_HjwOCnEStQRLaq-0V
+"""
+
+import numpy as np
+import pandas as pd
+import os
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+import pandas as pd
+
+import missingno as msno
+
+import warnings
+warnings.filterwarnings('ignore')
+
+df = pd.read_csv("/content/ecommerce_sales_analysis.csv")
+df.head()
+
+df.tail()
+
+df.shape
+
+df.info()
+
+df.describe().T
+
+df.describe().T.plot(kind='bar')
+
+df.isnull().sum()
+
+sns.heatmap(df.isnull())
+
+df.duplicated().sum()
+
+numeric_df = df.select_dtypes(include=['number'])
+
+plt.figure(figsize=(12, 6))
+sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm')
+plt.title('Correlation Heatmap')
+plt.show()
+
+df.columns.to_list()
+
+import plotly.express as px
+
+columns = ['product_id',
+'product_name',
+'category',
+'price',
+'review_score',
+'review_count',
+'sales_month_1',
+'sales_month_2',
+'sales_month_3',
+'sales_month_4',
+'sales_month_5',
+'sales_month_6',
+'sales_month_7',
+'sales_month_8',
+'sales_month_9',
+'sales_month_10',
+'sales_month_11',
+'sales_month_12',]
+
+for column in columns:
+
+  if df[column].dtype == 'object' or df[column].dtype == 'category':
+    column_counts = df[column].value_counts().reset_index()
+    column_counts.columns = [column, 'count']
+
+    fig = px.bar(
+        column_counts,
+        x=column,
+        y='count',
+        title=f'Distribution of {column}',
+        labels={column: column, 'count': 'Count'},
+        text='count'
+    )
+
+    fig.update_layout(
+        xaxis_title=column,
+        yaxis_title='Count',
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        title_font=dict(size=18, family="Arial"),
+
+        xaxis={'categoryorder':'total descending'}
+    )
+
+    fig.show()
+
+  elif df[column].dtype == 'int64' or df[column].dtype == 'float64':
+
+    fig = px.histogram(
+        df,
+        x=column,
+        title=f'Distribution of {column}',
+        labels={column: column, 'count': 'Count'},
+
+    )
+
+    fig.update_layout(
+        xaxis_title=column,
+        yaxis_title='Count',
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        title_font=dict(size=18, family="Arial")
+    )
+
+    fig.show()
+
+df
+
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud, STOPWORDS
+from collections import Counter
+import pandas as pd
+
+stop_words_list = set(STOPWORDS)
+
+counts = Counter(df["category"].dropna().apply(lambda x: str(x)))
+
+wcc = WordCloud(
+    background_color="black",
+    width=1600, height=800,
+    max_words=2000,
+    stopwords=stop_words_list
+)
+wcc.generate_from_frequencies(counts)
+
+plt.figure(figsize=(10, 5), facecolor='k')
+plt.imshow(wcc, interpolation='bilinear')
+plt.axis("off")
+plt.tight_layout(pad=0)
+plt.show()