<jupyter_start><jupyter_text>Pandas<jupyter_code>import numpy as np
import pandas as pd
label=['a','b','c']
my_data=[10,20,30]
arr=np.array(my_data)
d={'a':10,'b':20,'c':30}<jupyter_output><empty_output><jupyter_text>Series by list<jupyter_code>pd.Series(data=my_data)
pd.Series(data=my_data,index=label)
pd.Series(my_data,label)<jupyter_output><empty_output><jupyter_text>Series by array<jupyter_code>pd.Series(arr,label)<jupyter_output><empty_output><jupyter_text>Seies By dictionar<jupyter_code>pd.Series(d)
d

pd.Series(data=[sum,print,len])
ser1=pd.Series([1,2,3,4],['USA','GERMANY','USSR','JAPAN'])
ser1
ser2=pd.Series([1,2,3,4],['USA','GERMANY','Italy','JAPAN'])
ser2
ser1['USA']
ser3=pd.Series(data=label)
ser3
ser3[0]
ser1 + ser2     # Integers converted into float in order to retain all the in fo<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1863799283154122, 'fraction_numerical': 0.037037037037037035, 'mean_word_length': 12.737704918032787, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '443941', 'license': 'Apache-2.0', 'n_tokens_mistral': 377, 'n_tokens_neox': 365, 'n_words': 44, 'path': 'panada Series.ipynb', 'repo_name': 'tanishkasinghal/python', 'size': '837'}