df.groupby(['col']).agg({'func': func})
to
df.groupby(['col']).agg(func=func)
Here is what the above code is Doing:
1. groupby(‘col’) – groups the dataframe by the column ‘col’
2. agg({‘func’: func}) – applies the function ‘func’ to the column ‘func’
3. agg(func=func) – applies the function ‘func’ to the column ‘func’
settings.py MESSAGE_TAGS = { messages.ERROR: ‘danger’, } Here is what the above code is Doing: 1. We’re importing the messages framework from django.contrib.messages. 2. We’re adding a MESSAGE_TAGS setting to our settings.py file. 3. We’re setting the ERROR message tag to ‘danger’. Now, when we use the messages framework, we can use the ‘danger’ tag…
i = round(0.5) print(i) Here is what the above code is Doing: 1. It’s creating a variable called ‘i’ and assigning it the value of 0.5. 2. It’s printing the value of ‘i’. 3. It’s rounding the value of ‘i’ to the nearest integer. 4. It’s printing the value of ‘i’ again. The output of…
from django.db import models # Create your models here. class Contact(models.Model): name = models.CharField(max_length=50) email = models.CharField(max_length=50) contact = models.CharField(max_length=50) content = models.TextField() def __str__(self): return self.name + ‘ ‘ + self.email Here is what the above code is Doing: 1. We are creating a class named Contact. 2. We are creating a name field,…
from datetime import datetime now = datetime.now() print (now.strftime(“%Y-%m-%d %H:%M:%S”)) Output: 2020-06-19 10:34:45 Here is what the above code is Doing: 1. We imported the datetime class from the datetime module. 2. We created a datetime object now by calling datetime.now(). 3. We printed the current date and time by calling now.strftime(“%Y-%m-%d %H:%M:%S”). The now.strftime(“%Y-%m-%d…
![check if entry is NaT]](https://myedukit.com/wp-content/uploads/check-if-entry-is-NaT-768x496.jpg)
>>> import numpy as np >>> import pandas as pd >>> pd.isnull(np.datetime64(‘NaT’)) True Here is what the above code is Doing: 1. Importing the NumPy and pandas libraries 2. Creating a NumPy datetime64 object with the value NaT 3. Using the pandas library to check if the NumPy datetime64 object is null
df = pd.read_csv(df_name, converters={‘column_name’: eval}) Here is what the above code is Doing: 1. We’re reading in the csv file using the pandas read_csv function. 2. We’re specifying the column we want to convert to a list using the converters parameter. 3. We’re using the eval function to convert the string to a list.