In Python3 the StringIO and cStringIO modules are gone. In Python3 you should use: from PIL import Image import requests from io import BytesIO response = requests.get(url) img = Image.open(BytesIO(response.content))
Python dictionaries don’t support duplicate keys. One way around is to store lists or sets inside the dictionary. One easy way to achieve this is by using defaultdict: from collections import defaultdict data_dict = defaultdict(list) All you have to do is replace data_dict[regNumber] = details with data_dict[regNumber].append(details) and you’ll get a dictionary of lists.
This is creating a single integer that’s the concatenation of all the numbers in CaseNumberList, not a list of integers: IntCaseNumberList = int(“”.join(str(i) for i in CaseNumberList)) So when you try to ue IntCaseNumberList[i], it doesn’t work because it’s a number, not a list. To create a list of integers, you need to call int() … Read more
I’m way too lazy to type in all that data everytime I run your script, so I automated that part to make development and testing of it easier. Likewise, I think the easiest thing to do would be to use the collections module’s defaultdict class to keep track of what dates have been seen and … Read more
I couldn’t quite figure out why your go.Figure turns out the way it does. But if you reshape your data from wide to long and unleash px.bar you’ll get a shorter, cleaner code and arguably a much better visual result. We can talk more details later, but you’ll find a complete snippet right after this … Read more
It turns out that the computer in question that would display the bad plot had McAfee Antivirus installed on it and this caused the issue with scanning the .exe and delaying the output of data to matplotlib. Once we added the .exe and necessary programs into the “exclusion” list, we were able to get the … Read more
The issue for your goal is your range that you create with linspace. The default number of samples is 50 for this method and they are evenly reparted so depending on your width, 2 might not be in this range. Use the num argument (third position) to increase this value and it will look better … Read more
There are several ways to approach controlling a matplotlib plot using ipywidgets. Below I’ve created the output I think you’re looking for using each of the options. The methods are listed in what feels like the natural order of discovery, however, I would recommend trying them in this order: 4, 2, 1, 3 Approach 1 … Read more
It is much easier if you split Year/month columns to separate series for each year import pandas as pd import matplotlib.pyplot as plt fig, axes = plt.subplots(figsize=(6,4)) df = pd.read_csv(“data.csv”) df2 = pd.pivot_table(df, index=”Month”, columns=[“Year”]) df2 = df2.reindex([‘January’, ‘February’, ‘March’, ‘April’, ‘May’, ‘July’, ‘August’, ‘September’, ‘October’, ‘November’, ‘December’]) df2.plot(ax=axes) fig.savefig(“plot.png”)
Matplotlib Graph Data