Implementation of pandas Data Index and Selection

We choose DataFrame, mostly at three levels: row, column, region, cell.

The corresponding methods are as follows:
1. Rows, Columns-- > df[]
2. Region-- > df.loc[], df.iloc[], df.ix[]
3. Cells-- > df.at[], df.iat[]

Start the exercises below:

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randn(6,4), index=list('abcdef'), columns=list('ABCD'))

1. df[]:

1-D
Row dimension:
Integer slices, label slices, < Boolean Array >
Column dimension:
Label Index, Label List, Callable

df[:3]
df['a':'c']
df[[True,True,True,False,False,False]] #  Front 3 Rows (Boolean array length equals number of rows) 
df[df['A']>0] # A Column value greater than 0 Line 
df[(df['A']>0) | (df['B']>0)] # A Column value greater than 0 Or B Columns larger than 0 Line 
df[(df['A']>0) & (df['C']>0)] # A Column value greater than 0 And C Columns larger than 0 Line 

df['A']
df[['A','B']]
df[lambda df: df.columns[0]] # Callable

2. df.loc[]

2-D, row after column
Row dimension:
Label Index, Label Slice, Label List, < Boolean Array > , Callable
Column dimension:
Label Index, Label Slice, Label List, < Boolean Array > , Callable

df.loc['a', :]
df.loc['a':'d', :]
df.loc[['a','b','c'], :]
df.loc[[True,True,True,False,False,False], :] #  Front 3 Rows (Boolean array length equals number of rows) 
df.loc[df['A']>0, :]
df.loc[df.loc[:,'A']>0, :]
df.loc[df.iloc[:,0]>0, :]
df.loc[lambda _df: _df.A > 0, :]

df.loc[:, 'A']
df.loc[:, 'A':'C']
df.loc[:, ['A','B','C']]
df.loc[:, [True,True,True,False]] #  Front 3 Column (Boolean array length equals number of rows) 
df.loc[:, df.loc['a']>0]     # a Line greater than 0 Columns of 
df.loc[:, df.iloc[0]>0]      # 0 Line greater than 0 Columns of 
df.loc[:, lambda _df: ['A', 'B']]

df.A.loc[lambda s: s > 0]

3. df.iloc[]

2-D, row after column
Row dimension:
Integer index, integer slice, integer list, < Boolean Array >
Column dimension:
Integer index, integer slice, integer list, < Boolean Array > , Callable

df.iloc[3, :]
df.iloc[:3, :]
df.iloc[[0,2,4], :]
df.iloc[[True,True,True,False,False,False], :] #  Front 3 Rows (Boolean array length equals number of rows) 
df.iloc[df['A']>0, :]    # ×   Why not?Can't figure it out! 
df.iloc[df.loc[:,'A']>0, :] # × 
df.iloc[df.iloc[:,0]>0, :] # × 
df.iloc[lambda _df: [0, 1], :]

df.iloc[:, 1]
df.iloc[:, 0:3]
df.iloc[:, [0,1,2]]
df.iloc[:, [True,True,True,False]] #  Front 3 Column (Boolean array length equals number of rows) 
df.iloc[:, df.loc['a']>0] # × 
df.iloc[:, df.iloc[0]>0] # × 
df.iloc[:, lambda _df: [0, 1]]

4. df.ix[]

2-D, row after column
Row dimension:
Integer index, integer slice, integer list,
Label Index, Label Slice, Label List,
< Boolean Array > ,
Callable
Column dimension:
Integer index, integer slice, integer list,
Label Index, Label Slice, Label List,
< Boolean Array > ,
Callable

df.ix[0, :]
df.ix[0:3, :]
df.ix[[0,1,2], :]

df.ix['a', :]
df.ix['a':'d', :]
df.ix[['a','b','c'], :]

df.ix[:, 0]
df.ix[:, 0:3]
df.ix[:, [0,1,2]]

df.ix[:, 'A']
df.ix[:, 'A':'C']
df.ix[:, ['A','B','C']]

5. df.at[]

Locate cells accurately
Row dimension:
Label Index
Column dimension:
Label Index

df[:3]
df['a':'c']
df[[True,True,True,False,False,False]] #  Front 3 Rows (Boolean array length equals number of rows) 
df[df['A']>0] # A Column value greater than 0 Line 
df[(df['A']>0) | (df['B']>0)] # A Column value greater than 0 Or B Columns larger than 0 Line 
df[(df['A']>0) & (df['C']>0)] # A Column value greater than 0 And C Columns larger than 0 Line 

6. df.iat[]

Locate cells accurately

Row dimension:
Integer Index
Column dimension:
Integer Index

df[:3]
df['a':'c']
df[[True,True,True,False,False,False]] #  Front 3 Rows (Boolean array length equals number of rows) 
df[df['A']>0] # A Column value greater than 0 Line 
df[(df['A']>0) | (df['B']>0)] # A Column value greater than 0 Or B Columns larger than 0 Line 
df[(df['A']>0) & (df['C']>0)] # A Column value greater than 0 And C Columns larger than 0 Line