Sort data by a single column in ascending order and save the sorted data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
sorted_data = data.sort_values('column1', ascending=True) # Sort data by 'column1' in ascending order
for index, row in sorted_data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
Sort data by multiple columns in different orders and save the sorted data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
sorted_data = data.sort_values(by=['column1', 'column2'], ascending=[True, False]) # Sort data by 'column1' in ascending order and 'column2' in descending order
for index, row in sorted_data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
Clean data by removing rows with missing values and save the cleaned data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
cleaned_data = data.dropna() # Remove rows with missing values
for index, row in cleaned_data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
For example, let's assume the original DataFrame data contains the following data:
column1 column2
0 Value1 1.0
1 Value2 NaN
2 Value3 3.0
3 Value4 4.0
4 Value5 NaN
After executing the code cleaned_data = data.dropna(), the resulting cleaned_data will be:
column1 column2
0 Value1 1.0
2 Value3 3.0
3 Value4 4.0
Clean data by replacing missing values with a default value and save the cleaned data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
cleaned_data = data.fillna('N/A') # Replace missing values with 'N/A'
for index, row in cleaned_data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
For example, let's assume the original DataFrame data contains the following data:
column1 column2
0 Value1 1.0
1 Value2 NaN
2 Value3 3.0
3 Value4 4.0
4 Value5 NaN
After executing the code cleaned_data = data.fillna('N/A'), the resulting cleaned_data will be:
column1 column2
0 Value1 1
1 Value2 N/A
2 Value3 3
3 Value4 4
4 Value5 N/A
Clean data by removing duplicate rows and save the cleaned data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
cleaned_data = data.drop_duplicates() # Remove duplicate rows
for index, row in cleaned_data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
Clean data by replacing specific values with another value and save the cleaned data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
cleaned_data = data.replace({'column1': {'Value 1': 'New Value', 'Value 2': 'New Value'}}) # Replace specific values in 'column1'
for index, row in cleaned_data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
Clean data by converting column data types and save the cleaned data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
data['column1'] = data['column1'].astype(int) # Convert 'column1' to integer data type
data['column2'] = data['column2'].astype(float) # Convert 'column2' to float data type
for index, row in data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
Clean data by removing leading and trailing whitespaces from string columns and save the cleaned data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
data['column1'] = data['column1'].str.strip() # Remove leading and trailing whitespaces from 'column1'
data['column2'] = data['column2'].str.strip() # Remove leading and trailing whitespaces from 'column2'
for index, row in data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
Clean data by applying custom cleaning functions to specific columns and save the cleaned data to a Django model:
import pandas as pd
from myapp.models import MyModel
data = pd.read_csv('data.csv')
def custom_cleaning_function(value):
# Define your custom cleaning logic
# Return the cleaned value
data['column1'] = data['column1'].apply(custom_cleaning_function) # Apply custom cleaning function to 'column1'
data['column2'] = data['column2'].apply(custom_cleaning_function) # Apply custom cleaning function to 'column2'
for index, row in data.iterrows():
my_model = MyModel(field1=row['column1'], field2=row['column2'])
my_model.save()
In these examples, we import data from a CSV file using pd.read_csv(). Then, we use various pandas functions such as sort_values(), dropna(), fillna(), drop_duplicates(), replace(), astype(), and str.strip() to sort and clean the data. Finally, we save the sorted and cleaned data to Django models by iterating over the resulting DataFrame and creating instances of the Django model.
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