import pandas as pd

dates1 = pd.date_range('2020-01-01', periods=6,freq="D")
dates2 = pd.date_range('2020-01-01', periods=4,freq="M")
dates3 = pd.date_range('2020-01-01', periods=2,freq="Y")
print(dates1)
print(dates2)
print(dates3)

# Creating a DataFrame by passing a numpy array, with an index and labeled columns:
df = pd.DataFrame(np.random.randn(6,4),
                  index=dates1,
                  columns=['I','II','III','IV'])
print(df)

# Data Parts
print('first row od dataframe:',df.head(1))
print('part of dataframe:',df.tail(3))

# Describe shows a quick statistic summary of your data
print('stats for dataframe:',df.describe())

# Sorting & Transposition
df2 = df.sort_index(axis=1, ascending=False)
print('transposition for sorted dataframe:',df2.T)
print('sorted dataframe no.2:',df2.sort_values(by="II"))

# Creating a DataFrame by passing a numpy array, 
#with an index and labeled columns:

dates = pd.date_range('20210101', periods=4)
df3 = pd.DataFrame(np.random.randn(4,3), 
index=dates, columns=list('XYZ'))
print(df3)

# Selection methods
print('values for X:')
print(df3['X'])
print('part of:')
print(df3[2:4])
print('Selection by Label:')
print(df3.loc[dates[3]])
print('get all the indexes and only two columns:')
print(df3.loc[:,['X','Y']])
print('if you want particular value')
print(df3.loc[dates[0],'X'])
print('Selection by Position:')
print(df3.iloc[3])
print(df3.iloc[3:4,0:2])
print(df3.iloc[[1,2,3],[0,2]])
print(df3[df3.Z > 0.05])