有没有办法从装有数据的字典中删除nan? [英] Is there a way to remove nan from a dictionary filled with data?

问题描述

我有一个字典，里面充满了我导入的两个文件中的数据，但是其中一些数据是nan出来的.如何使用nan删除数据?

I have a dictionary that is filled with data from two files I imported, but some of the data comes out as nan. How do I remove the pieces of data with nan?

我的代码是:

import matplotlib.pyplot as plt 
from pandas.lib import Timestamp
import numpy as np   
from datetime import datetime
import pandas as pd
import collections

orangebook = pd.read_csv('C:\Users\WEGWEIS_JAKE\Desktop\Work Programs\Code Files\products2.txt',sep='~', parse_dates=['Approval_Date'])
specificdrugs=pd.read_csv('C:\Users\WEGWEIS_JAKE\Desktop\Work Programs\Code Files\Drugs.txt',sep=',')

"""This is a dictionary that collects data from the .txt file
This dictionary has a key,value pair for every generic name with its corresponding approval date """
drugdict={}
for d in specificdrugs['Generic Name']:
    drugdict.dropna()
    drugdict[d]=orangebook[orangebook.Ingredient==d.upper()]['Approval_Date'].min()

我应该添加或删除此代码中的哪些内容，以确保字典中不存在键值为nan的键值对?

What should I add or take away from this code to make sure that there are no key,value pairs in the dictionary with a value of nan?

推荐答案

from math import isnan

如果nans被存储为键:

if nans are being stored as keys:

# functional
clean_dict = filter(lambda k: not isnan(k), my_dict)

# dict comprehension
clean_dict = {k: my_dict[k] for k in my_dict if not isnan(k)}

如果nans被存储为值:

if nans are being stored as values:

# functional
clean_dict = filter(lambda k: not isnan(my_dict[k]), my_dict)

# dict comprehension
clean_dict = {k: my_dict[k] for k in my_dict if not isnan(my_dict[k])}

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