<jupyter_start><jupyter_text>Question 1:Import the customer data into python.<jupyter_code>import pandas as pd
CustomerData=pd.read_csv('C:/Users/xskolantaa/Desktop/Customers.csv')
#print (CustomerData)<jupyter_output><empty_output><jupyter_text>Q2:Understand the data using different functions and attributes like shape, index, columns, dtypes.ndim, info(), get_dtype_counts etc<jupyter_code>#Head 
CustomerData.head
CustomerData.shape #denotes rows,columns
CustomerData.index #index of the dataset
CustomerData.columns #Number of columns
CustomerData.ndim #number of dimensions in DataFrame
CustomerData.info()
CustomerData.get_dtype_counts()<jupyter_output><empty_output><jupyter_text>Q3: What is percentage of missing values for customer Value variable<jupyter_code>missingData=CustomerData.isna()
missingData_mis=missingData.sum()
perce= missingData_mis/len(CustomerData)*100
print(perce.get('Customer_Value'))<jupyter_output>0.36<jupyter_text>Q4:Create two subsets with unique and duplicate values<jupyter_code>UniqSet= CustomerData.drop_duplicates(keep=False,inplace=False)
UniqSet

DiffSet=CustomerData[CustomerData.duplicated(['Customer_ID'])]
DiffSet<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09663865546218488, 'fraction_numerical': 0.008403361344537815, 'mean_word_length': 11.153061224489797, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '480409', 'license': 'MIT', 'n_tokens_mistral': 395, 'n_tokens_neox': 385, 'n_words': 74, 'path': 'PythonBasics/AssignmentsTechM/Assignment2.ipynb', 'repo_name': 'AnyMoment/PracticePython', 'size': '1190'}