Python的卓越灵活性和易用性使其成为最受欢迎的编程语言之一，尤其是对于数据处理和机器学习方面来说，其弱小的数据处理库和算法库使得python成为入门数据迷信的首选语言。在日常应用中，CSV，JSON和XML三种数据格式占据主导地位。上面我将针对三种数据格式来分享其疾速解决的办法。
CSV数据
CSV是存储数据的最罕用办法。在Kaggle较量的大部分数据都是以这种形式存储的。咱们能够应用内置的Python csv库来读取和写入CSV。通常，咱们会将数据读入列表列表。
看看上面的代码。当咱们运行csv.reader()所有CSV数据变得可拜访时。该csvreader.next()函数从CSV中读取一行; 每次调用它，它都会挪动到下一行。咱们也能够应用for循环遍历csv的每一行for row in csvreader 。确保每行中的列数雷同，否则，在解决列表列表时，最终可能会遇到一些谬误。
import csv filename = "my_data.csv"fields = [] rows = [] # Reading csv file with open(filename, 'r') as csvfile: # Creating a csv reader object csvreader = csv.reader(csvfile) # Extracting field names in the first row fields = csvreader.next() # Extracting each data row one by one for row in csvreader: rows.append(row) # Printing out the first 5 rows for row in rows[:5]: print(row)在Python中写入CSV同样容易。在单个列表中设置字段名称，并在列表列表中设置数据。这次咱们将创立一个writer()对象并应用它将咱们的数据写入文件，与读取时的办法根本一样。
import csv # Field names fields = ['Name', 'Goals', 'Assists', 'Shots'] # Rows of data in the csv file rows = [ ['Emily', '12', '18', '112'], ['Katie', '8', '24', '96'], ['John', '16', '9', '101'], ['Mike', '3', '14', '82']]filename = "soccer.csv"# Writing to csv file with open(filename, 'w+') as csvfile: # Creating a csv writer object csvwriter = csv.writer(csvfile) # Writing the fields csvwriter.writerow(fields) # Writing the data rows csvwriter.writerows(rows)咱们能够应用Pandas将CSV转换为疾速单行的字典列表。将数据格式化为字典列表后，咱们将应用该dicttoxml库将其转换为XML格局。咱们还将其保留为JSON文件！
import pandas as pdfrom dicttoxml import dicttoxmlimport json# Building our dataframedata = {'Name': ['Emily', 'Katie', 'John', 'Mike'], 'Goals': [12, 8, 16, 3], 'Assists': [18, 24, 9, 14], 'Shots': [112, 96, 101, 82] }df = pd.DataFrame(data, columns=data.keys())# Converting the dataframe to a dictionary# Then save it to filedata_dict = df.to_dict(orient="records")with open('output.json', "w+") as f: json.dump(data_dict, f, indent=4)# Converting the dataframe to XML# Then save it to filexml_data = dicttoxml(data_dict).decode()with open("output.xml", "w+") as f: f.write(xml_data)JSON数据
JSON提供了一种简洁且易于浏览的格局，它放弃了字典式构造。就像CSV一样，Python有一个内置的JSON模块，使浏览和写作变得非常简单！咱们以字典的模式读取CSV时，而后咱们将该字典格局数据写入文件。
import jsonimport pandas as pd# Read the data from file# We now have a Python dictionarywith open('data.json') as f: data_listofdict = json.load(f)# We can do the same thing with pandasdata_df = pd.read_json('data.json', orient='records')# We can write a dictionary to JSON like so# Use 'indent' and 'sort_keys' to make the JSON# file look nicewith open('new_data.json', 'w+') as json_file: json.dump(data_listofdict, json_file, indent=4, sort_keys=True)# And again the same thing with pandasexport = data_df.to_json('new_data.json', orient='records')正如咱们之前看到的，一旦咱们取得了数据，就能够通过pandas或应用内置的Python CSV模块轻松转换为CSV。转换为XML时，能够应用dicttoxml库。具体代码如下：
import jsonimport pandas as pdimport csv# Read the data from file# We now have a Python dictionarywith open('data.json') as f: data_listofdict = json.load(f)# Writing a list of dicts to CSVkeys = data_listofdict[0].keys()with open('saved_data.csv', 'wb') as output_file: dict_writer = csv.DictWriter(output_file, keys) dict_writer.writeheader() dict_writer.writerows(data_listofdict)XML数据
XML与CSV和JSON有点不同。CSV和JSON因为其既简略又疾速，能够不便人们进行浏览，编写和解释。而XML占用更多的内存空间，传送和贮存须要更大的带宽，更多存储空间和更久的运行工夫。然而XML也有一些基于JSON和CSV的额定性能：您能够应用命名空间来构建和共享构造规范，更好地传承，以及应用XML、DTD等数据表示的行业标准化办法。
要读入XML数据，咱们将应用Python的内置XML模块和子模ElementTree。咱们能够应用xmltodict库将ElementTree对象转换为字典。一旦咱们有了字典，咱们就能够转换为CSV，JSON或Pandas Dataframe！具体代码如下：
import xml.etree.ElementTree as ETimport xmltodictimport jsontree = ET.parse('output.xml')xml_data = tree.getroot()xmlstr = ET.tostring(xml_data, encoding='utf8', method='xml')data_dict = dict(xmltodict.parse(xmlstr))print(data_dict)with open('new_data_2.json', 'w+') as json_file: json.dump(data_dict, json_file, indent=4, sort_keys=True)