Python data analysis pandas read data

I. Reading of three data files

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Second, csv, tsv, txt file reading

1) CSV file reading:

Syntax format: pandas.read_csv(file path)
The contents of the CSV file are as follows:

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import pandas as pd
file_path = "e:\\pandas_study\\"
content = pd.read_csv(file_path)

()  # Returns the first 5 rows of data by default
(3)  # Return the first 3 rows of data
  # Returns a tuple (total number of rows, total number of columns), the total number of rows does not include the header rows

    # Returns the index, which is an iterable object <class ''>

    # Return all column names Index(['name', 'age', 'origin'], dtype='object')

  # The return is the data type of each column
name and surname    object
(a person's) age     int64
place of ancestry    object
dtype: object

2）CSV file reading:

Syntax format: pandas.read_csv(file path)
The contents of the CSV file are as follows:

在这里插入图片描述

import pandas as pd
file_path = "e:\\pandas_study\\"

content = pd.read_csv(file_path,sep='\t',header = None ,names= ['name','age','adress'])
# Parameter Description:
# header = None means no header line
# sep='\t' means to remove spaces from the separators
# names= ['name','age','address'] with columns customized to 'name','age','address' in that order

()  # Returns the first 5 rows of data by default
(3)  # Return the first 3 rows of data
  # Returns a tuple (total number of rows, total number of columns), the total number of rows does not include the header rows

    # Returns the index, which is an iterable object <class ''>

    # Return all column names Index(['name', 'age', 'origin'], dtype='object')

  # The return is the data type of each column

Third, excel file reading

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import pandas as pd
file_path = "e:\\pandas_study\\"
content = pd.read_excel(file_path)

()  # Returns the first 5 rows of data by default
(3)  # Return the first 3 rows of data
  # Returns a tuple (total number of rows, total number of columns), the total number of rows does not include the header rows

    # Returns the index, which is an iterable object <class ''>

    # Return all column names Index(['name', 'age', 'origin'], dtype='object')

  # The return is the data type of each column
name and surname    object
(a person's) age     int64
place of ancestry    object
dtype: object

IV. Database table reading

Grammar: pandas.read_sql(sql statement, database connection object)
Data objects can be created by connecting to mysql or oracle according to modules such as pymysql,cx_oracle.

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