<jupyter_start><jupyter_text>INTRODUCTION TO PANDAS Pandas is a Python package providing fast, flexible, and expressive data structures designed tomake working with “relational” or “labeled” data both easy and intuitive. It aims to be thefundamental high-level building block for doing practical, real world data analysis in Python. Installing pandaspip install pandas  OR conda install pandas  A Quick view<jupyter_code>#importing modules
import pandas as pd # this convention is followed by many.
import numpy as np
import matplotlib.pyplot as plt # for plotting graphs

np.random.seed(0)

values = np.random.randn(100) # a array with 100 values

s = pd.Series(values) # generating dataframe
s.plot(kind="hist",title="normal distribution of random values") # a histogram plot 
plt.show()
#finding mean,standard deviation, min, max counts
s.describe()
# an example
df = pd.DataFrame({'A': [1, 2, 1, 4, 3, 5, 2, 3, 4, 1],
 'B': [12, 14, 11, 16, 18, 18, 22, 13, 21, 17],
 'C': ['a', 'a', 'b', 'a', 'b', 'c', 'b', 'a', 'b', 'a']})
df.describe()<jupyter_output><empty_output><jupyter_text>Note : that since C is not a numerical column, it is excluded from the output.<jupyter_code>df['C'].describe() # info about just column "C"<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12430278884462151, 'fraction_numerical': 0.02948207171314741, 'mean_word_length': 5.375634517766497, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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