python using pandas to implement the filter function way

data=[2:5] 
#Here.[2:5]denote3go to the end of the line5content of the row，[]The first start is0，Indicates the first row of the data

data = df[(df['Column name 1']== ‘column value1')]
# When matching multiple conditions
data_many=df[(df['Column name 1']== ‘column value1')&(df['Column name 2']==‘column value2')]
# When matching multiple values
data_many=df[df['Column name 1'] in [‘(be) worth1', 'value 2'，......]]

# Pattern matching with a value at the beginning
cond=df['Column name'].('Value')
$ Pattern matching with a value in the middle
cond=df['Column name'].('Value')

# Filter the data based on between two values:
cond=df[(df['Column name 1']>‘column value1')&(df['Column name 1']<‘column value2')] 

print(ridership_df.loc['05-05-11','R003'])
# Or
print(ridership_df.iloc[4,0])
 
# Results.
1608

print()

import pandas as pd
 
df = ({'BoolCol': [1, 2, 3, 3, 4],'attr': [22, 33, 22, 44, 66]},index=[10,20,30,40,50])
print(df)
a = df[(==3)&(==22)].()
b = df[(==3)&(==22)].index[0]
c = df[(==3)&(==22)].
print(a)

print(date_frame)                # Print the full display
print(date_frame.shape)            # Get rows, columns meta-anchor of df
print(date_frame.head(2))        # The first 2 lines
print(date_frame.tail(2))        # 2 lines after
 
print(date_frame.())        # Get only the list of indexes for df
print(date_frame.())        # Get a list of column names for df only
print(date_frame.())        # get onlydfA list of all the values of the（two-dimensional list）

# Used provided that the index and columns of the dataframe are in numbers, utilizing the drop() and range() functions.
 
(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')
 
# axis = 0 to delete rows; axis = 1 to delete columns.
 
# want to delete multiple rows/columns, with range can be, for example, to delete the first 3 rows, drop(range(0,3), axis = 0 (default zero, can not write)) can be.

import datetime
import pandas as pd
 
dictDate = {'date': ['2019-11-01 19:30', '2019-11-30 19:00']}
df = (dictDate)
df['datetime'] = pd.to_datetime(df['date'])
df['today'] = df['datetime'].apply(lambda x: ('%Y%m%d'))
df['tomorrow'] = (df['datetime'] + (days=1)).('%Y%m%d')

The apply() function of # pandas can be applied to a Series or an entire DataFrame, and also automatically iterates over the entire Series or DataFrame, running the specified function on each element.
def add_extra(nationality, extra):
    if nationality != "Han.":
        return extra
    else:
        return 0
 
df['ExtraScore'] = (add_extra, args=(5,))
df['ExtraScore'] = (add_extra, extra=5)
df['Extra'] = (lambda n, extra : extra if n == 'Han' else 0, args=(5,))
 
def add_extra2(nationaltiy, **kwargs):
    return kwargs[nationaltiy]
       
df['Extra'] = (add_extra2, the Han dynasty (206 BC-220 AD)=0, classifier for a chapter in old Chinese fictional novels=10, harbor=5)

import datetime
import pandas as pd
def f(x):
    x = str(x)[:8]
    if x !='n':
        gf = (x, "%Y%m%d")
        x = ("%Y-%m-%d")
    return x
    
def f2(x):
    if str(x) not in [' ', 'nan']:
        dd = (str(x), "%Y/%m/%d")
        x = ("%Y-%m-%d")
    return x  
 
def test():
    df = ()
    df1 = pd.read_csv("600694_gf.csv")
    df2=pd.read_csv("")
    df['date1'] =df2['DateTime'].map(f2) 
    df['date2'] =df1['date'].map(f)
    df.to_csv('')