Implementation of the Pandas type conversion asype

The data type correspondence in Python and in Pandas is as follows:

Fruit data is pure data that can be converted into numbers
asype is basically used as two kinds of numbers into simple strings, simple strings into numbers, containing other non-numeric strings can not be converted by asype.
Other methods need to be introduced for the transformation, which also results in the following custom function methods

astype() is the most common and versatile data type conversion method

import pandas as pd
 
df = ([['liver','E',89,21,24,64],
                   ['Arry','C',36,37,37,57],
                   ['Ack','A',57,60,18,84],
                   ['Eorge','C',93,96,71,78],
                   ['Oah','D',65,49,61,86]
                  ], 
                   columns = ['name','team','Q1','Q2','Q3','Q4'])
 
res = 
 
df.('int32').dtypes # dtype('int32')
({'Q1':'int32','Q2':'int32'}).dtypes

Results Showcase

res

extensions

# The following are some examples of use.
('int64') # Index type conversion
('int32') # All data converted to int32
({'col1':'int32'}) # Specified fields to specified types
('int64')
('int64',copy = False) # Not associated with original data
df['name'].astype('object')
data['Q4'].astype('float')
('datatime64[ns]') # Converted to time type
data['Status'].astype('bool')

data type

will return the data type of each field and the type of the DataFrame as a whole

If it's a Series, you need to use the

import pandas as pd
 
df = ([['liver','E',89,21,24,64],
                   ['Arry','C',36,37,37,57],
                   ['Ack','A',57,60,18,84],
                   ['Eorge','C',93,96,71,78],
                   ['Oah','D',65,49,61,86]
                  ], 
                   columns = ['name','team','Q1','Q2','Q3','Q4'])

 
s = (['One','Two','Three'])

Results Showcase

When the format of the data does not have the conditions for conversion to the target type, the data needs to be processed first

For example, "89.3%" is a string that is converted to a number by removing the percent sign.

# Convert text such as "89.3%" to a floating point number
(lambda x:('%','')).astype('float')/100

When loading data, you can specify the type of each column of the data.

import pandas as pd
 
# Assign uniform types to all fields
df = (data, dtype = 'float32')
# Specify each field separately
df = pd.read_excel(data, dtype = {'team':'string','Q1':'int32'})

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