import pandas as pd
pd.__version__

print(help(pd))

dir(pd)

import pandas as pd;   import numpy as np

pd.Series([1, 2, 3, 4, 5])

pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])

pd.Series(np.array([1, 2, 3, 4, 5]))

pd.Series(np.array([1, 2, 3, 4, 5])) + pd.Series(np.array([5, 4, 3, 2, 1]))

d = {"b": 1, "a": 0, "c": 2}; pd.Series(d)

df = pd.DataFrame(np.random.randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())
df

import pandas as pd
d = {"one": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
    "two": pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"])
    }
df = pd.DataFrame(d); print(type(df));  print('\n'); df.info();   print('\n');  df

import pandas as pd
import numpy as np

df=pd.DataFrame([[1, 2, 3, 4],
                [5, 6, 7, 8],
                [9, 20, 22, 34]],
                index=[0,1,2],
                columns=['A', 'B', 'C', 'D'])
df

# source: Yahoo Finance
stock = pd.DataFrame([['tech', 'AAPL', 157.25, 1.23, 26.11, 2539, 0.58, 184.01],
                     ['tech', 'MSFT', 261.47, 0.93, 26.98, 1942, 0.95, 333.00],
                     ['engr', 'XOM', 93.87, 1.08, 10.27, 391, 3.68, 103.28],
                     ['engr', 'COP', 109.45, 1.38, 8.87, 137, 1,75],
                     ['fin', 'BAC',33.47, 1.40, 10.46, 269, 2.62, 42.41],
                     ['fin', 'JPM',114.51, 1.10, 9.18, 336, 3.52, 138.80],
                     ['fin', 'WMT',335.50, 1.39, 7.53, 114, 3.00, 394.07]])
stock

stock.columns=['sector', 'ticker', 'price', 'beta', 'pe', 'mktcap', 'dy', 'target']
stock.index = ["a", "b", "c", "d", "e", "f", "g"]
stock

stock.info()

stock.index = [0, 1, 2, 3, 4, 5, 6]
stock

stock.drop('target',axis=1)

# but this wont affect the orginal data
stock

# must put down inplace option
stock.drop('target', axis=1, inplace=True)
stock

stock['target'] = [184.01, 333.00, 103.28, 75.00, 42.41, 138.80, 394.07]
stock

stock2 = stock.drop(6, axis=0)
stock2

newstock = pd.DataFrame([['ind', 184.71, 1.00, 14.61, 98, 2.63, 214.00]], 
                        index=['CAT'],
                        columns=['sector', 'price', 'beta', 'pe', 'mktcap', 'dy', 'target'])
                        
newstock

stock3 = stock.append(newstock)
stock3

stock3 = pd.concat([stock, newstock])
stock3

stock.loc[6, 'ticker'] = 'GS'
stock

stock['price'].replace(335.50, 435.50, inplace=True)

stock

print(stock['ticker']); print('\n');
print(stock[['ticker']]); print('\n');
print(stock.ticker); print('\n');
print(stock.loc[:, 'ticker']); print('\n');
print(stock.iloc[:, 1]); print('\n');
print(stock.iloc[:, -1]); print('\n');

stock.loc[:, 'ticker':'beta']

stock.loc[:, :]

stock.loc[0:2, 'beta']

stock.loc[3, "ticker"]

stock.loc[5, "mktcap"] > stock.loc[6, "mktcap"]

stock.index

stock.describe()

print(stock['price'].mean()); 
print(stock['beta'].median()); 
print(stock['dy'].std()); 
print(stock['mktcap'].max()); 

stock.groupby('sector').mean().round(2)

stock.set_index('ticker', inplace=True)


stock.loc['JPM', 'mktcap'] > stock.loc['GS', 'mktcap']

stock