python 文件读取

csv 读取

# import required libraries
import csv
import pandas as pd
from pprint import pprint

def print_basic_csv(file_name, delimiter=','):
    """This function extracts and prints csv content from given filename
       Details: https://docs.python.org/2/library/csv.html
    Args:
        file_name (str): file path to be read
        delimiter (str): delimiter used in csv. Default is comma (',')

    Returns:
        None

    """
    csv_rows = list()
    csv_attr_dict = dict()
    csv_reader = None

    # read csv
    csv_reader = csv.reader(open(file_name, 'r'), delimiter=delimiter)
        
    # iterate and extract data    
    for row in csv_reader:
        print(row)
        csv_rows.append(row)
    
    # prepare attribute lists
    for col in csv_rows[0]:
        csv_attr_dict[col]=list()
    
    # iterate and add data to attribute lists
    for row in csv_rows[1:]:
        csv_attr_dict['sno'].append(row[0])
        csv_attr_dict['fruit'].append(row[1])
        csv_attr_dict['color'].append(row[2])
        csv_attr_dict['price'].append(row[3])
    
    # print the result
    print("\n\n")
    print("CSV Attributes::")
    pprint(csv_attr_dict)
            


def print_tabular_data(file_name,delimiter=","):
    """This function extracts and prints tabular csv content from given filename
       Details: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
    Args:
        file_name (str): file path to be read
        delimiter (str): delimiter used in csv. Default is comma ('\t')

    Returns:
        None

    """
    df = pd.read_csv(file_name,sep=delimiter)
    print(df)
    
'''
# example
print_basic_csv(r'tabular_csv.csv')
print_tabular_data(r'tabular_csv.csv')

'''

json

# import required libraries
import json
import pandas as pd

def print_nested_dicts(nested_dict,indent_level=0):
    """This function prints a nested dict object
    Args:
        nested_dict (dict): the dictionary to be printed
        indent_level (int): the indentation level for nesting
    Returns:
        None

    """
    
    for key, val in nested_dict.items():
        if isinstance(val, dict):
          print("{0} : ".format(key))
          print_nested_dicts(val,indent_level=indent_level+1)
        elif isinstance(val,list):
            print("{0} : ".format(key))
            for rec in val:
                print_nested_dicts(rec,indent_level=indent_level+1)
        else:
          print("{0}{1} : {2}".format("\t"*indent_level,key, val))

def extract_json(file_name,do_print=True):
    """This function extracts and prints json content from a given file
    Args:
        file_name (str): file path to be read
        do_print (bool): boolean flag to print file contents or not

    Returns:
        None

    """
    try:
        json_filedata = open(file_name).read() 
        json_data = json.loads(json_filedata)
        
        if do_print:
            print_nested_dicts(json_data)
    except IOError:
        raise IOError("File path incorrect/ File not found")
    except ValueError:
        ValueError("JSON file has errors")
    except Exception:
        raise

def extract_pandas_json(file_name,orientation="records",do_print=True):
    """This function extracts and prints json content from a file using pandas
       This is useful when json data represents tabular, series information
    Args:
        file_name (str): file path to be read
        orientation (str): orientation of json file. Defaults to records
        do_print (bool): boolean flag to print file contents or not

    Returns:
        None

    """
    try:
        df = pd.read_json(file_name,orient=orientation)
        
        if do_print:
            print(df)
    except IOError:
        raise IOError("File path incorrect/ File not found")
    except ValueError:
        ValueError("JSON file has errors")
    except Exception:
        raise

'''
#example
extract_json(r'sample_json.json')
extract_pandas_json(r'pandas_json.json')
'''

xml

# import required libraries
import json
import pandas as pd

def print_nested_dicts(nested_dict,indent_level=0):
    """This function prints a nested dict object
    Args:
        nested_dict (dict): the dictionary to be printed
        indent_level (int): the indentation level for nesting
    Returns:
        None

    """
    
    for key, val in nested_dict.items():
        if isinstance(val, dict):
          print("{0} : ".format(key))
          print_nested_dicts(val,indent_level=indent_level+1)
        elif isinstance(val,list):
            print("{0} : ".format(key))
            for rec in val:
                print_nested_dicts(rec,indent_level=indent_level+1)
        else:
          print("{0}{1} : {2}".format("\t"*indent_level,key, val))

def extract_json(file_name,do_print=True):
    """This function extracts and prints json content from a given file
    Args:
        file_name (str): file path to be read
        do_print (bool): boolean flag to print file contents or not

    Returns:
        None

    """
    try:
        json_filedata = open(file_name).read() 
        json_data = json.loads(json_filedata)
        
        if do_print:
            print_nested_dicts(json_data)
    except IOError:
        raise IOError("File path incorrect/ File not found")
    except ValueError:
        ValueError("JSON file has errors")
    except Exception:
        raise

def extract_pandas_json(file_name,orientation="records",do_print=True):
    """This function extracts and prints json content from a file using pandas
       This is useful when json data represents tabular, series information
    Args:
        file_name (str): file path to be read
        orientation (str): orientation of json file. Defaults to records
        do_print (bool): boolean flag to print file contents or not

    Returns:
        None

    """
    try:
        df = pd.read_json(file_name,orient=orientation)
        
        if do_print:
            print(df)
    except IOError:
        raise IOError("File path incorrect/ File not found")
    except ValueError:
        ValueError("JSON file has errors")
    except Exception:
        raise

'''
#example
extract_json(r'sample_json.json')
extract_pandas_json(r'pandas_json.json')
'''