622_581_252_159.py

import numpy as np
import pandas as pd

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
  for filename in filenames:
    print(os.path.join(dirname, filename))

# Commented out IPython magic to ensure Python compatibility.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %matplotlib inline
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

df=pd.read_csv('/content/Amazon Sale Report.csv')
df.shape

df.head()

df.tail()

df.info()

df.drop(['New', 'PendingS'], axis=1, inplace=True)

df.info()

pd.isnull(df)

pd.isnull(df).sum()

df.shape

df.dropna(inplace=True)
df.shape

df.shape

df.columns

df['ship-postal-code']=df['ship-postal-code'].astype('int')

df['ship-postal-code'].dtype

df['Date']=pd.to_datetime (df['Date'])

df.columns

df.rename(columns={'Qty':'Quantity'})

df.describe()

df.describe(include='object')

df[['Qty','Amount']].describe()

df.columns

ax=sns.countplot(x='Size', data=df)

ax=sns.countplot(x='Size', data=df)

for bars in ax.containers:
  ax.bar_label(bars)

df.groupby(['Size'], as_index=False)['Qty'].sum().sort_values(by='Qty',ascending=False)

S_Qty=df.groupby(['Size'], as_index=False)['Qty'].sum().sort_values(by='Qty', ascending=False)

sns.barplot(x='Size', y='Qty', data=S_Qty)

sns.countplot(data=df, x='Courier Status', hue='Status')

plt.figure(figsize=(10, 5))

ax=sns.countplot(data=df, x='Courier Status', hue='Status')
plt.show()

df['Size'].hist()

df['Category'] = df['Category'].astype(str)
column_data = df['Category']
plt.figure(figsize=(10, 5))
plt.hist(column_data, bins=10, edgecolor='Black')
plt.xticks(rotation=90)
plt.show()

B2B_Check = df['B2B'].value_counts()
plt.pie(B2B_Check, labels=B2B_Check, autopct='%1.1f%%')
plt.show()

B2B_Check = df['B2B'].value_counts()
plt.pie(B2B_Check, labels=B2B_Check.index, autopct='%1.1f%%')
plt.show

a1 = df['Fulfilment'].value_counts()
fig, ax = plt.subplots()
ax.pie(a1, labels=a1.index, autopct='%1.1f%%', radius=0.7, wedgeprops=dict(width=0.6))
ax.set(aspect="equal")
plt.show()

x_data = df['Category']
y_data = df['Size']

plt.scatter(x_data, y_data)
plt.xlabel('Category')
plt.ylabel('Size')
plt.title('Scatter Plot')
plt.show()

plt.figure(figsize=(12, 6))
sns.countplot(data=df, x='ship-state')
plt.xlabel('ship-state')
plt.ylabel('count')
plt.title('Distribution of State')
plt.xticks(rotation=90)
plt.show()

top_10_state = df['ship-state'].value_counts().head(10)
plt.figure(figsize=(12, 6))
sns.countplot(data=df[df['ship-state'].isin(top_10_state.index)], x='ship-state')
plt.xlabel('ship-state')
plt.ylabel('count')
plt.title('Distribution of State')
plt.xticks(rotation=45)
plt.show()