<jupyter_start><jupyter_text>Loading Dataset  Importing Libraries<jupyter_code>import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))<jupyter_output>/kaggle/input/hm-stores-dataset/HM_all_stores.csv<jupyter_text>Loading Dataset<jupyter_code>df = pd.read_csv('../input/hm-stores-dataset/HM_all_stores.csv')
df.head(10)<jupyter_output><empty_output><jupyter_text>Dataset Information<jupyter_code>df.info()<jupyter_output><class 'pandas.core.frame.DataFrame'>
RangeIndex: 4292 entries, 0 to 4291
Data columns (total 21 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   storeCode       4292 non-null   object 
 1   storeClass      4289 non-null   object 
 2   name            4290 non-null   object 
 3   phone           4140 non-null   object 
 4   city            4292 non-null   object 
 5   country         4292 non-null   object 
 6   countryCode     4292 non-null   object 
 7   longitude       4292 non-null   float64
 8   latitude        4292 non-null   float64
 9   timeZoneIndex   4289 non-null   float64
 10  Mon_open_hours  4258 non-null   object 
 11  Tue_open_hours  4258 non-null   object 
 12  Wed_open_hours  4258 non-null   object 
 13  Thu_open_hours  4258 non-null   object 
 14  Fri_open_hours  4258 non-null   object 
 15  Sat_open_hours  4258 non-null   object 
 16  Sun_open_hours  3148 non-null   object 
 17  streetName1     4292 non-null   o[...]<jupyter_text>Countries in which store is present<jupyter_code>unqCountries= df['country'].unique()
unqCountries<jupyter_output><empty_output><jupyter_text>**Total number of countries in which store is present**<jupyter_code>df['country'].nunique()<jupyter_output><empty_output><jupyter_text>Total number of store in each country<jupyter_code>pd.set_option('display.max_row',10) ## to view all replace 10 with None 
df['country'].value_counts()<jupyter_output><empty_output><jupyter_text>Store Classes<jupyter_code>df['storeClass'].unique()<jupyter_output><empty_output><jupyter_text>**Total number of classes**<jupyter_code>df['storeClass'].nunique()<jupyter_output><empty_output><jupyter_text>Total cities in which H&M store is present<jupyter_code>unq_city = df['city'].nunique()


unq_city<jupyter_output><empty_output><jupyter_text>Counties, Cities and the number of store in respective city<jupyter_code>pd.set_option('display.max_row',20) ## write None instead of 20 to view all
df[['city','country']].value_counts()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10430586488492948, 'fraction_numerical': 0.0467706013363029, 'mean_word_length': 4.0753295668549905, 'pattern_counts': {'":': 0, '<': 40, '<?xml version=': 0, '>': 40, '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': '10', 'dir': 'jupyter-structured-clean-dedup', 'id': '92807', 'license': 'Apache-2.0', 'n_tokens_mistral': 1037, 'n_tokens_neox': 924, 'n_words': 207, 'path': 'h-m-data-analysis.ipynb', 'repo_name': 'IAMOSAMA111/Data-Analysis', 'size': '2694'}