First, Pandas query data in several ways
- df[] selects by rows and columns, in which case only rows or columns can be selected at a time.
- method, which queries the rows and columns based on their labeled values
- method, query based on the numerical position of rows and columns, and locate based on indexes
- methodologies
Second, the use of Pandas query data methods
- Querying data with a single label value
- Batch query using a list of values
- Range queries using numeric intervals
- Querying with Conditional Expressions
- Call function query
take note of
The above query method applies to both rows and columns
##########################################
df[]
>>> df=((25).reshape([5,5]),index=['A','B','C','D','E'],columns=['c1','c2','c3','c4','c5'])
>>> df
c1 c2 c3 c4 c5
A 0.499404 0.082137 0.472568 0.649200 0.121681
B 0.564688 0.102398 0.374904 0.091373 0.495510
C 0.319272 0.720225 0.979103 0.910206 0.766642
D 0.478346 0.311616 0.466326 0.045612 0.258015
E 0.421653 0.577140 0.103048 0.235219 0.550336
##########################################
# Get the c1 and c2 columns
df[['c1','c2']]
>>> df[['c1','c2']]
c1 c2
A 0.499404 0.082137
B 0.564688 0.102398
C 0.319272 0.720225
D 0.478346 0.311616
E 0.421653 0.577140
##########################################
# Get the c1 column
df.c1
>>> df.c1 A 0.499404 B 0.564688 C 0.319272 D 0.478346 E 0.421653 Name: c1, dtype: float64
##########################################
# Get the data indexed as rows A-C
df['A':'C']
>>> df['A':'C']
c1 c2 c3 c4 c5
A 0.499404 0.082137 0.472568 0.649200 0.121681
B 0.564688 0.102398 0.374904 0.091373 0.495510
C 0.319272 0.720225 0.979103 0.910206 0.766642
##########################################
# Get 2-3 rows of data
df[1:3]
>>> df[1:3]
c1 c2 c3 c4 c5
B 0.564688 0.102398 0.374904 0.091373 0.495510
C 0.319272 0.720225 0.979103 0.910206 0.766642
##########################################
Methodological inquiries
1, the use of numerical intervals for range queries
It's kind of like slicing a list.
>>> ['A':'D',:]
c1 c2 c3 c4 c5
A 0.499404 0.082137 0.472568 0.649200 0.121681
B 0.564688 0.102398 0.374904 0.091373 0.495510
C 0.319272 0.720225 0.979103 0.910206 0.766642
D 0.478346 0.311616 0.466326 0.045612 0.258015
##########################################
2, a single label value query
Similar coordinate search
>>> ['A','c2'] 0.08213716245372071
##########################################
3、Use the list of batch query
>>> [['A','B','D'],['c1','c3']]
c1 c3
A 0.499404 0.472568
B 0.564688 0.374904
D 0.478346 0.466326
##########################################
4. Use of conditional expression queries
>>> [df['c2']>0.5,:]
c1 c2 c3 c4 c5
C 0.319272 0.720225 0.979103 0.910206 0.766642
E 0.421653 0.577140 0.103048 0.235219 0.550336
>>> df[(df['c2']>0.2) & (df['c3'] < 0.8)]
c1 c2 c3 c4 c5
D 0.478346 0.311616 0.466326 0.045612 0.258015
E 0.421653 0.577140 0.103048 0.235219 0.550336
##########################################
5, the use of function query
def query_my_data(df):
return ((df['c3']>0.2) & (df["c4"]<0.8))
[query_my_data, :]
c1 c2 c3 c4 c5
B 0.845310 0.545040 0.946026 0.106405 0.984376
C 0.844622 0.947104 0.878854 0.377638 0.175846
E 0.139952 0.420424 0.364295 0.012773 0.307853
##########################################
Methodological inquiries
Similarly, locating by index
# Extract 2-3 rows, 1-2 columns of data
[1:3,0:2]
>>> [1:3,0:2]
c1 c2
B 0.564688 0.102398
C 0.319272 0.720225
##########################################
# Extract the second third row, fourth column of data
[[1,2],[3]]
c4 B 0.091373 C 0.910206
##########################################
# Extract a single value at a specified location
[3,4]
>>> [3,4] 0.2580148841605816
summarize
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