pandas+dataframe implements row and column selection and slicing operations

This time I will bring you pandas dataframe to implement row selection and slicing operations. What are the precautions for pandas dataframe to implement row selection and slicing operations? The following is a practical case, let's take a look.

The select in SQL is based on the name of the column; Pandas is more flexible. It can not only be selected based on the column name, but also based on the

position (number, in which position) the column is located. Row and column, please note that the position of pandas rows and columns starts from 0). Related functions are as follows:

1) loc, based on column label, can select specific rows (based on row index);

2) iloc, based on row/column position ;

3) at, quickly locates the elements of the DataFrame according to the specified row index and column label;

4) iat, similar to at, except that it is positioned based on position;

5) ix, a mixture of loc and iloc, supports both label and position;

Example

import pandas as pd
import numpy as np
df = pd.DataFrame({'total_bill': [16.99, 10.34, 23.68, 23.68, 24.59],
          'tip': [1.01, 1.66, 3.50, 3.31, 3.61],
          'sex': ['Female', 'Male', 'Male', 'Male', 'Female']})
# data type of columns
print df.dtypes
# indexes
print df.index
# return pandas.Index
print df.columns
# each row, return array[array]
print df.values
print df

sex      object
tip      float64
total_bill  float64
dtype: object
RangeIndex(start=0, stop=5, step=1)
Index([u'sex', u'tip', u'total_bill'], dtype='object')
[['Female' 1.01 16.99]
 ['Male' 1.66 10.34]
 ['Male' 3.5 23.68]
 ['Male' 3.31 23.68]
 ['Female' 3.61 24.59]]
   sex  tip total_bill
0 Female 1.01    16.99
1  Male 1.66    10.34
2  Male 3.50    23.68
3  Male 3.31    23.68
4 Female 3.61    24.59

print df.loc[1:3, ['total_bill', 'tip']]
print df.loc[1:3, 'tip': 'total_bill']
print df.iloc[1:3, [1, 2]]
print df.iloc[1:3, 1: 3]

  total_bill  tip
1    10.34 1.66
2    23.68 3.50
3    23.68 3.31
  tip total_bill
1 1.66    10.34
2 3.50    23.68
3 3.31    23.68
  tip total_bill
1 1.66    10.34
2 3.50    23.68
  tip total_bill
1 1.66    10.34
2 3.50    23.68

Incorrect representation:

print df.loc[1:3, [2, 3]]#.loc仅支持列名操作

KeyError: 'None of [[2, 3]] are in the [columns]'

print df.loc[[2, 3]]#.loc可以不加列名，则是行选择

  sex  tip total_bill
2 Male 3.50    23.68
3 Male 3.31    23.68

print df.iloc[1:3]#.iloc可以不加第几列，则是行选择

sex  tip total_bill
1 Male 1.66    10.34
2 Male 3.50    23.68

print df.iloc[1:3, 'tip': 'total_bill']

TypeError: cannot do slice indexing on <class> with these indexers [tip] of <type></type></class>

print df.at[3, 'tip']
print df.iat[3, 1]
print df.ix[1:3, [1, 2]]
print df.ix[1:3, ['total_bill', 'tip']]

3.31
3.31
  tip total_bill
1 1.66    10.34
2 3.50    23.68
3 3.31    23.68
  total_bill  tip
1    10.34 1.66
2    23.68 3.50
3    23.68 3.31

print df.ix[[1, 2]]#行选择

  sex  tip total_bill
1 Male 1.66    10.34
2 Male 3.50    23.68

print df[1: 3]
print df[['total_bill', 'tip']]
# print df[1:2, ['total_bill', 'tip']] # TypeError: unhashable type

sex  tip total_bill
1 Male 1.66    10.34
2 Male 3.50    23.68
  total_bill  tip
0    16.99 1.01
1    10.34 1.66
2    23.68 3.50
3    23.68 3.31
4    24.59 3.61

# I believe you have mastered the method after reading the case in this article. Please pay attention for more exciting things. Other related articles on php Chinese website!

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