nan

python pandas dataframe : fill nans with a conditional mean

由 无人久伴 提交于 2020-04-12 10:49:30
问题 I have the following dataframe: import numpy as np import pandas as pd df = pd.DataFrame(data={'Cat' : ['A', 'A', 'A','B', 'B', 'A', 'B'], 'Vals' : [1, 2, 3, 4, 5, np.nan, np.nan]}) Cat Vals 0 A 1 1 A 2 2 A 3 3 B 4 4 B 5 5 A NaN 6 B NaN And I want indexes 5 and 6 to be filled with the conditional mean of 'Vals' based on the 'Cat' column, namely 2 and 4.5 The following code works fine: means = df.groupby('Cat').Vals.mean() for i in df[df.Vals.isnull()].index: df.loc[i, 'Vals'] = means[df.loc[i

python pandas dataframe : fill nans with a conditional mean

由 自作多情 提交于 2020-04-12 10:47:45
问题 I have the following dataframe: import numpy as np import pandas as pd df = pd.DataFrame(data={'Cat' : ['A', 'A', 'A','B', 'B', 'A', 'B'], 'Vals' : [1, 2, 3, 4, 5, np.nan, np.nan]}) Cat Vals 0 A 1 1 A 2 2 A 3 3 B 4 4 B 5 5 A NaN 6 B NaN And I want indexes 5 and 6 to be filled with the conditional mean of 'Vals' based on the 'Cat' column, namely 2 and 4.5 The following code works fine: means = df.groupby('Cat').Vals.mean() for i in df[df.Vals.isnull()].index: df.loc[i, 'Vals'] = means[df.loc[i

python pandas dataframe : fill nans with a conditional mean

由 霸气de小男生 提交于 2020-04-12 10:41:30
问题 I have the following dataframe: import numpy as np import pandas as pd df = pd.DataFrame(data={'Cat' : ['A', 'A', 'A','B', 'B', 'A', 'B'], 'Vals' : [1, 2, 3, 4, 5, np.nan, np.nan]}) Cat Vals 0 A 1 1 A 2 2 A 3 3 B 4 4 B 5 5 A NaN 6 B NaN And I want indexes 5 and 6 to be filled with the conditional mean of 'Vals' based on the 'Cat' column, namely 2 and 4.5 The following code works fine: means = df.groupby('Cat').Vals.mean() for i in df[df.Vals.isnull()].index: df.loc[i, 'Vals'] = means[df.loc[i

Removing NAN's from numpy 2-D arrays

由 雨燕双飞 提交于 2020-04-11 05:18:26
问题 Similar to this question I would like to remove some NAN's from a 2-D numpy array. However, instead of removing an entire row that has NAN's I want to remove the corresponding element from each row of the array. For example (using list format for simplicity) x=[ [1,2,3,4], [2,4,nan,8], [3,6,9,0] ] would become x=[ [1,2,4], [2,4,8], [3,6,0] ] I can imagine using a numpy.where to figure out where in each row the NAN's appear and then use some loops and logic statements to make a new array from

Checking if particular value (in cell) is NaN in pandas DataFrame not working using ix or iloc

由 无人久伴 提交于 2020-04-05 15:44:47
问题 Lets say I have following pandas DataFrame : import pandas as pd df = pd.DataFrame({"A":[1,pd.np.nan,2], "B":[5,6,0]}) Which would look like: >>> df A B 0 1.0 5 1 NaN 6 2 2.0 0 First option I know one way to check if a particular value is NaN , which is as follows: >>> df.isnull().ix[1,0] True Second option (not working) I thought below option, using ix , would work as well, but it's not: >>> df.ix[1,0]==pd.np.nan False I also tried iloc with same results: >>> df.iloc[1,0]==pd.np.nan False

sklearn Logistic Regression ValueError: X has 42 features per sample; expecting 1423

由 谁说我不能喝 提交于 2020-03-03 10:03:39
问题 I'm stuck trying to fix an issue. Here is what I'm trying to do : I'd like to predict missing values (Nan) (categorical one) using logistic regression. Here is my code : df_1 : my dataset with missing values only in the "Metier" feature (missing values I'm trying to predict) X_train = pd.get_dummies(df_1[df_1['Metier'].notnull()].drop(columns='Metier'),drop_first = True) X_test = pd.get_dummies(df_1[df_1['Metier'].isnull()].drop(columns='Metier'),drop_first = True,dummy_na = True) Y_train =

java中的NAN和INFINITY

由 你。 提交于 2020-02-28 22:08:41
java浮点数运算中有两个特殊的情况：NAN、INFINITY。 1、INFINITY： 在浮点数运算时，有时我们会遇到除数为0的情况，那java是如何解决的呢？ 我们知道，在整型运算中，除数是不能为0的，否则直接运行错误。但是在浮点数运算中，引入了无限这个概念，我们来看一下Double和Float中的定义。 Double： public static final double POSITIVE_INFINITY = 1.0 / 0.0; public static final double NEGATIVE_INFINITY = -1.0 / 0.0; Float： public static final float POSITIVE_INFINITY = 1.0f / 0.0f; public static final float NEGATIVE_INFINITY = -1.0f / 0.0f; 那么这些值对运算会有什么影响呢？ 我们先思考一下下面几个问题： Float和Double中的无限有什么区别？ 例如无限乘以0会是什么？ 0除以0又会有什么结果？ 再来看下面的示例： public static void main(String[] args) { float fPos=Float.POSITIVE_INFINITY; float fNeg=Float.NEGATIVE

JavaScript: what is NaN, Object or primitive?

由 空扰寡人 提交于 2020-02-27 23:11:51
问题 what is NaN, Object or primitive? NaN - Not a Number 回答1: NaN is a primitive Number value. Just like 1 , 2 , etc. 回答2: It's a primitive. You can check in a number of ways: typeof NaN gives "number," not "object." Add a property, it disappears. NaN.foo = "hi"; console.log(NaN.foo) // undefined NaN instanceof Number gives false (but we know it's a number, so it must be a primitive). It wouldn't really make sense for NaN to be an object, because expressions like 0 / 0 need to result in NaN , and

When to use NaN or +/-Infinity?

由 守給你的承諾、 提交于 2020-02-23 09:32:13
问题 What are the benefits of NaN , PositiveInfinity or NegativeInfinity for float and double ? When should we use or avoid them? If there are constants like these, why does float.Parse("a") throw an error rather than returning float.NaN ? How is NaN different than null ? Why is division by zero even possible for floating types? 回答1: Infinities are used because they are part of the arithmetic system supported by floating point. There are various operations, such as dividing by zero, in which

Negation in np.select() condition

由 别等时光非礼了梦想. 提交于 2020-02-23 07:13:43
问题 Here is my code: import pandas as pd import numpy as np df = pd.DataFrame({ 'var1': ['a', 'b', 'c',np.nan, np.nan], 'var2': [1, 2, np.nan , 4, np.nan] }) conditions = [ (not(pd.isna(df["var1"]))) & (not(pd.isna(df["var2"]))), (pd.isna(df["var1"])) & (pd.isna(df["var2"]))] choices = ["No missing", "Both missing"] df['Result'] = np.select(conditions, choices, default=np.nan) Output: File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1478, in __nonzero__ f"The truth
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