How python pandas reads rows or columns of data using loc and iloc

Create a DataFrame

data = {'name':['Zhang San', 'Li Si', 'Wang Wu', 'Zhao Liu'],'age':[20, 21, 22, 23], 'gender': [0, 1, 1, 1], 'stature': [165, 189, 178, 160], 'year': [2000, 2002, 2003, 1993]}
df = (data)
print (df)

The results of the run are as follows:

name age gender stature year
0 Zhang 20 0 165 2000
1 Li Si 21 1 189 2002
2 Wang Wu 22 1 178 2003
3 Zhao Liu 23 1 160 1993

I. Using the loc method to read data

loc: access by tagged values (column and row index values), support for single-value access or sliced queries, you can also specify the return column variable

1.1 Reading the value of a row or column

# 1. Read the second line, the name of the second line is "1".
df1= [1]
'''
name the fourth child in the family
age 21
gender 1
stature 189
year 2002
Name: 1, dtype: object
'''
# 2. Read the second column, which has the name age.
df2 = [ : ,"age"]
'''
0    20
1    21
2    22
3    23
Name: age, dtype: int64
'''
# 3. read a value at the same time, read the value with row number 2 and column name
df3 = [2, 'name']
# 'Wang Wu'

1.2 Reading a region

# Read the values in rows 1 through 2, age through stature columns.
df4 = [ 1:2, "age":"stature"]
df4

1.3 Screening by condition

Single-criteria filtering

# Single-criteria filtering: reading people older than 20 years old
df5 = [  > 20]

Multi-criteria filtering

# Multi-criteria filtering: read people older than 20 and with a stature of 180 or more.
df5 = [( > 20) & (> 180)]
df5

Conditions + Slices

# Read people older than 20, and only display name and feature
df5 = [  > 20, ['name', 'stature']]
df5

II. Reading data using the iloc method

iloc: accessed via row index and column index positions (numeric index), supports single-value access or sliced queries

2.1 Reading the value of a row or column

# 1. read the value of the second line, the first line starts at 0
df1= [1]
'''
name the fourth child in the family
age 21
gender 1
stature 189
year 2002
Name: 1, dtype: object
'''
# 2. Read the second column, the first one starts at 0
df2 = [ : , 1]
'''
0    20
1    21
2    22
3    23
Name: age, dtype: int64
'''
# 3. Reads a value at the same time, reads the value in row 3, column 1. The first column starts at 0
df3 = [2, 0]
# 'Wang Wu'

2.2 Reading data from a region

# Read rows 2 and 3, columns 3 and 4 #
df1 = [1:3, 2:4]
df1

summarize

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