DEV Community: brhett

llama_index integration with Milvus database

brhett — Tue, 13 Feb 2024 01:52:57 +0000

Feb 9 '24 Comments: 1 Answers: 0

I'm working on setting up indexing for my Milvus database. I'm not keen on using the default indexing it offers; instead, I'm looking to get the llama_index up and running.

I gave implementing llama_index a shot, but now I'm kinda second-guessing if I got it right.

If any of y'all…

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Answer: Deprecation Warning with groupby.apply in pandas

brhett — Tue, 13 Feb 2024 01:14:40 +0000

Feb 9 '24

Please try this:

import pandas as pd
from io import StringIO
# Create DataFrame
df = pd.read_csv(StringIO(data))

# Group by 'StartDate', 'Commodity', 'DealType' and calculate necessary aggregates
agg_df = df.groupby(['StartDate', 'Commodity']).agg(
    total_MTMValue=('MTMValue', 'sum'),
    total_FixedPriceStrike_times_Quantity=('FixedPrice', lambda x: (x * df.loc[x.index, 'Quantity']).sum()),
    total_Quantity=('Quantity', 'sum')
).reset_index()

# Calculate 'FloatPrice'
agg_df['FloatPrice'] = -(agg_df['total_MTMValue'] -

…

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Answer: Picking a variable's category from a dataframe

brhett — Tue, 13 Feb 2024 01:14:22 +0000

Feb 12 '24

please try this:

import seaborn as sns

# Load the dataset
df = sns.load_dataset("tips")

# Filter the dataframe for 'Dinner' time
df_dinner = df[df['time'] == 'Dinner']

# Perform aggregation on the filtered dataframe
result = df_dinner.agg({'total_bill': ['sum', 'min', 'max', 'mean']})

# Display the result
print(result)

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Answer: Apply find_peaks() function using groupby() in a pandas dataframe

brhett — Tue, 13 Feb 2024 01:13:40 +0000

Feb 12 '24

import pandas as pd
import numpy as np
from scipy.signal import find_peaks
def find_peaks_in_group(group):
    # Find peaks
    peaks, peak_properties = find_peaks(group['RFPower'], prominence=1, height=0.7)
    
    # Initialize columns to store peak data
    group['peaks'] = False
    group['peak_heights'] = 0.0
    
    # Use iloc to correctly assign values based on relative indices
    if len(peaks) >

…

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Answer: Is it possible to create foreign keys with pandas to_sql

brhett — Tue, 13 Feb 2024 01:00:01 +0000

Feb 12 '24

While the to_sql method in pandas doesn't directly handle foreign key constraints, you can still create them after loading data into your SQL database. If you're using SQLAlchemy, you can follow these steps:

Write your DataFrames to SQL tables using to_sql.
Execute an ALTER TABLE SQL statement to define…

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