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