I am going to convert Python pandas dataframe to dataframe in R. I found out few libraries for this problem http://pandas.pydata.org/pandas-docs/stable/r_interface.html which is rpy2 But I couldn't find the methods for saving or transfer it to R. Firstly I tried "to_csv" df_R = com.convert_to_r_dataframe(df_total) df_R.to_csv(direc+"/qap/detail_summary_R/"+"distance_"+str(g
我将在Python中将Python熊猫数据框转换为数据框。我发现这个问题很少有库 http://pandas.pydata.org/pandas-docs/stable/r_interface.html 这是rpy2 但我找不到保存或转移到R的方法。 首先我试着“to_csv” df_R = com.convert_to_r_dataframe(df_total) df_R.to_csv(direc+"/qap/detail_summary_R/"+"distance_"+str(gp_num)+".csv",sep = ",") 但它给了我一个错误 "AttributeError: 'DataFrame' object has no attribu
R has its own format that is significantly more expressive than csv (knows about factors, for example). The extension is usually .Rdata, and it is manipulated from R using the load and save functions. I was wondering if the python pandas library know about this format? If not, is there another format (better than csv) for exchange between pandas and R? I used to think for the longest time t
R有自己的格式比csv更具表现力（例如了解因素）。 扩展名通常是.Rdata，并且使用load和save功能从R进行操作。 我想知道如果python熊猫图书馆知道这种格式？ 如果不是，熊猫和R之间是否有另一种格式（比csv好）？ 我曾经想过，需要一个R实例来反序列化R对象的最长时间 - 加载一个保存的R对象或一组对象，等同于读取（可能是压缩的二进制文件）数据流并对其进行反序列化。 但达沃证明我错了。 在他的CPAN模块Statistics-R
I have been wondering... If I am reading, say, a 400MB csv file into a pandas dataframe (using read_csv or read_table), is there any way to guesstimate how much memory this will need? Just trying to get a better feel of data frames and memory... df.memory_usage() will return how much each column occupies: >>> df.memory_usage() Row_ID 20906600 Household_ID 20906600 Ve
我一直在想......如果我正在读取一个400MB的csv文件到一个熊猫数据框中（使用read_csv或read_table），有什么办法来猜测这将需要多少内存？ 试图获得更好的数据帧和内存感受... df.memory_usage()将返回每列占用多少： >>> df.memory_usage() Row_ID 20906600 Household_ID 20906600 Vehicle 20906600 Calendar_Year 20906600 Model_Year 20906600 ... 要包含索引，请传递i
When deleting a column in a DataFrame I use: del df['column_name'] and this works great. Why can't I use: del df.column_name As you can access the column/Series as df.column_name , I expect this to work. It's difficult to make del df.column_name work simply as the result of syntactic limitations in Python. del df[name] gets translated to df.__delitem__(name) under the covers by Py
当删除DataFrame中的列时，我使用： del df['column_name'] 这很有用。 为什么我不能使用： del df.column_name 正如你可以访问列/ Series作为df.column_name ，我期望这个工作。 由于Python中的语法限制，很难让del df.column_name正常工作。 del df[name]被Python翻译为df.__delitem__(name) 。 在熊猫做这件事的最好方法是使用drop ： df = df.drop('column_name', 1) 其中1是轴编号（ 0表示行， 1表示列）。
I'm writing a library, and I can construct expressions using objects from my library. For example, x and y are instances from my library, and I can construct expressions like: # below is a simplified version of my class class MySymbol(object): import random _random_value = random.randint(1,4) def __init__(self, value): self.value = value def __add__(self, symbol):
我正在编写一个库，我可以使用库中的对象构造表达式。 例如， x和y是来自我的库的实例，我可以构造如下表达式： # below is a simplified version of my class class MySymbol(object): import random _random_value = random.randint(1,4) def __init__(self, value): self.value = value def __add__(self, symbol): return MySymbol(self.value + symbol.value) def __mul__(self,
Coming from a C# background the naming convention for variables and method names are usually either CamelCase or Pascal Case: // C# example string thisIsMyVariable = "a" public void ThisIsMyMethod() In Python, I have seen the above but I have also seen underscores being used: # python example this_is_my_variable = 'a' def this_is_my_function(): Is there a more preferable, definitive coding st
来自C＃背景的变量和方法名称的命名约定通常是CamelCase或Pascal案例： // C# example string thisIsMyVariable = "a" public void ThisIsMyMethod() 在Python中，我已经看到了上面的内容，但我也看到了下划线被使用： # python example this_is_my_variable = 'a' def this_is_my_function(): 是否有更适合Python的编码风格？ 请参阅Python PEP 8。 函数名称应该是小写字母，必要时用下划线分隔单词以提高可读性。 mi
This question already has an answer here: Parsing values from a JSON file? 7 answers Convert the string object to a JSON object. Ex: import json jData = json.loads('{"success":true,"key":"Syv77d","link":"https://file.io/Syv77d","expiry":"14 days"}') jData["link"]
这个问题在这里已经有了答案： 从JSON文件解析值？ 7个答案 将字符串对象转换为JSON对象。 例如： import json jData = json.loads('{"success":true,"key":"Syv77d","link":"https://file.io/Syv77d","expiry":"14 days"}') jData["link"]
This question already has an answer here: Parsing values from a JSON file? 7 answers I think this is what @Jean-François Fabre tried to indicate: import json response = """ { "age":[ {"#":"1","age":10}, {"#":"2","age":12}, {"#":"3","age":16}, {"#":"4","age":3} ], "age2":[ {"#":"1","age":10}, {"#":"2","age":12}, {
这个问题在这里已经有了答案： 从JSON文件解析值？ 7个答案 我认为@ Jean-FrançoisFabre试图表明： import json response = """ { "age":[ {"#":"1","age":10}, {"#":"2","age":12}, {"#":"3","age":16}, {"#":"4","age":3} ], "age2":[ {"#":"1","age":10}, {"#":"2","age":12}, {"#":"3","age":16}, {"#":"4","age":3} ],
This question already has an answer here: Parsing values from a JSON file? 7 answers Let's create a dictionary: >>> d = {'guitar':'Jerry', 'drums':'Mickey' } Now, let's dump it to a file: >>> import json >>> json.dump(d, open('1.json', 'w')) Now, let's read it back in: >>> json.load(open('1.json', 'r')) {'guitar': 'Jerry', 'drums': 'Mickey
这个问题在这里已经有了答案： 从JSON文件解析值？ 7个答案 我们来创建一个字典： >>> d = {'guitar':'Jerry', 'drums':'Mickey' } 现在，让我们将其转储到一个文件中： >>> import json >>> json.dump(d, open('1.json', 'w')) 现在，让我们回读一下： >>> json.load(open('1.json', 'r')) {'guitar': 'Jerry', 'drums': 'Mickey'} 更好地照顾文件句柄 上面的例子说明了json
This question already has an answer here: Parsing values from a JSON file? 7 answers This is a JSON file. Importing it using json.load(path) will give you a Python dict that has the exact same structure you see in your file. That is a JSON file but it is not complete. Some part is missing. You can use JSON LINT http://jsonlint.com/ to validate a json file. For parsing json in python u
这个问题在这里已经有了答案： 从JSON文件解析值？ 7个答案 这是一个JSON文件。 使用json.load(path)导入它会给你一个Python dict ，它具有你在文件中看到的完全相同的结构。 这是一个JSON文件，但它不完整。 有些部分缺失。 您可以使用JSON LINT http://jsonlint.com/来验证json文件。 在python中解析json你可以使用json库的细节可以在这篇文章中找到。 https://dzone.com/articles/python-reading-json-file 你