Pandas Data Index and selection

We choose the DataFrame from these three levels: rows, regions, cells.
The corresponding method of use is as follows:
A. Row, column--df[]
Two. Area--df.loc[], df.iloc[], df.ix[]
Three. Cell--df.at[], df.iat[]

Here's how to start the exercise:

Import NumPy as NP Import  = PD. DataFrame (Np.random.randn (6,4), index=list ('abcdef'), columns=list ('ABCD  '))

1. DF[]:

One-dimensional
Row dimension:
Integer slices, label slices, < Boolean arrays >
Levi degree:
Label index, label list, callable

Df[:3]df['a':'C']df[[true,true,true,false,false,false]]#first three rows (boolean array length equals number of rows)df[df['A']>0]#rows with a column value greater than 0df[(df['A']>0) | (df['B']>0)]#a column with a value greater than 0, or a row with column B greater than 0df[(df['A']>0) & (df['C']>0)]#rows with a column value greater than 0 and column C greater than 0

df['a']df[['a','B'  ]]df[Lambda#  callable

2. DF.Loc[]

Two-dimensional, first-after column
Row dimension:
Label index, label Slice, label list, < Boolean array >, callable
Levi degree:
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],:]#first three 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]]#first three columns (boolean array length equals number of rows)df.loc[:, df.loc['a']>0]#column A row is greater than 0df.loc[:, df.iloc[0]>0]#0 rows greater than 0 columnsdf.loc[:,Lambda_DF: ['A','B']]

Df. a.loc[Lambda s:s > 0]

3. DF.Iloc[]

Two-dimensional, pre-column
row dimension:
     integer index, integer slice, integer list, < Boolean array,
Levi:
    integer index, integer slice, integer list, < Boolean array >, callable

Df.iloc[3,:]df.iloc[:3,:]df.iloc[[0,2,4#  first three rows (boolean array length equals number of rows)DF.ILOC[DF ['A']>0,:]       #x why not? I can't figure it out! df.iloc[df.loc[:,'A'#xdf.iloc[df.iloc[:,0]>0,: ]  #xdf.iloc[Lambda _df: [0, 1],:]

df.iloc[:, 1]df.iloc[:, 0:3]df.iloc[:, [0,# # The first three columns (the length of the Boolean array equals the number of rows)Df.iloc [:, df.loc['a'#xdf.iloc[:, df.iloc[0]>0]  #x  Lambda _df: [0, 1]]

4. DF.IX[]

Two-dimensional, pre-column
row dimension:
     integer index, integer slice, integer list,
    tag index, label slice, label list,
    < Boolean array >,
     Callable
Levi:
     integer index, integer slice, integer list,
    tag index, label slice, label list,
    < Boolean array >,
     Callable

df.ix[0,:]df.ix[0:3,:]df.ix[[0,[+],:]df.ix['a',:] df.ix['a':'d',:]df.ix[['  a','b','C'],:

df.ix[:, 0]df.ix[:, 0:3]df.ix[:, [0,'A'  a':'C']df.ix[:, ['a ','B','C']

5. DF. at[]

Precise positioning of cells
Row dimension:
Label Index
Levi degree:
Label Index

df.at['a'a']

6. DF.IAT[]

Precise positioning of cells

Row dimension:
Integer index
Levi degree:
Integer index

Df.iat[0, 0]

Pandas Data Index and selection