nan

问题 I've got a dataset containing a lot of missing values (NAN). I want to use linear or multilinear regression in python and fill all the missing values. You can find the dataset here: Dataset I have used f_regression(X_train, Y_train) to select which feature should I use. first of all I convert df['country'] to dummy then used important features then I have used regression but the results Not good. I have defined following functions to select features and missing values: def select_features

问题 I've got a dataset containing a lot of missing values (NAN). I want to use linear or multilinear regression in python and fill all the missing values. You can find the dataset here: Dataset I have used f_regression(X_train, Y_train) to select which feature should I use. first of all I convert df['country'] to dummy then used important features then I have used regression but the results Not good. I have defined following functions to select features and missing values: def select_features

问题 In my DataFrame, there are columns including values of null and NaN respectively, such as: df = spark.createDataFrame([(1, float('nan')), (None, 1.0)], ("a", "b")) df.show() +----+---+ | a| b| +----+---+ | 1|NaN| |null|1.0| +----+---+ Are there any difference between those? How can they be dealt with? 回答1: null values represents "no value" or "nothing", it's not even an empty string or zero. It can be used to represent that nothing useful exists. NaN stands for "Not a Number", it's usually

问题 Doing some optimziation on a piece of code, the correctness of the code depending on how the compiler handle NaNs. I read the IEEE-754 rules on NaN, which states: The comparisons EQ, GT, GE, LT, and LE, when either or both operands is NaN returns FALSE. The comparison NE, when either or both operands is NaN returns TRUE. Are the above rules enforced in C/C++? 回答1: C/C++ does not require specific floating-point representation and does not require that any comparison against NaN is false . In C

问题 Updated with a DataFrame that repros my exact issue I have an issue where NaN appearing in my indexes is leading to non-unique rows (since NaN !== NaN ). I need to drop all rows where NaN occurs in the index. My previous question had an example DataFrame with a single NaN row, however the original solution did not resolve my issue as it did not meet this poorly advertised requirement: (Note that in the actual data I have thousands of such rows, including duplicate rows since NaN !== NaN so

问题 Updated with a DataFrame that repros my exact issue I have an issue where NaN appearing in my indexes is leading to non-unique rows (since NaN !== NaN ). I need to drop all rows where NaN occurs in the index. My previous question had an example DataFrame with a single NaN row, however the original solution did not resolve my issue as it did not meet this poorly advertised requirement: (Note that in the actual data I have thousands of such rows, including duplicate rows since NaN !== NaN so

问题 Sorry I cannot share the data. I tried to make test data but it does not gives same error or different missing values as described below. Added more info at bottom about pd.NA I am loading data with code: df = pd.read_csv("C:/data.csv") When loading data I am getting this warning: C:\Users\User1\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3063: DtypeWarning: Columns (162,247,274,292,304,316,321,335,345,347,357,379,389,390,393,395,400,401,420,424,447

问题 I have dictionary and created Pandas using cars = pd.DataFrame.from_dict(cars_dict, orient='index') and sorted the index (columns in alphabetical order cars = cars.sort_index(axis=1) After sorting I noticed the DataFrame has NaN and I wasn't sure if the really np.nan values? print(cars.isnull().any()) and all column shows false. I have tried different method to convert those "NaN" values to zero which is what I want to do but non of them is working. I have tried replace and fillna methods and

问题 I have dictionary and created Pandas using cars = pd.DataFrame.from_dict(cars_dict, orient='index') and sorted the index (columns in alphabetical order cars = cars.sort_index(axis=1) After sorting I noticed the DataFrame has NaN and I wasn't sure if the really np.nan values? print(cars.isnull().any()) and all column shows false. I have tried different method to convert those "NaN" values to zero which is what I want to do but non of them is working. I have tried replace and fillna methods and

问题 I am working on yii2 . In my javascript, I have a formula through which I am getting some data. The data is then passed to chart and chart renders it. var arry_kwh = []; arry_kwh = <?php echo json_encode($dataPointskWh, JSON_NUMERIC_CHECK); ?>; var arry_kwh_diff = []; var i = 0; for (; i < arry_kwh.length - 1; i++) { arry_kwh_diff[i] = { label: arry_kwh[i].label + ((arry_kwh[i + 1].label - arry_kwh[i].label) / 2), y: (arry_kwh[i + 1].y - arry_kwh[i].y) }; } console.log(JSON.stringify(arry_kwh