chatterboc
chat1
import pandas as pd
import itertools
def generate_combinations(input_file, output_file):
# Load the CSV file into a pandas DataFrame
df = pd.read_csv(input_file)
# Drop any rows where all columns are empty
df.dropna(how='all', inplace=True)
# Gather unique non-empty entries in each column
columns_data = [df[col].dropna().unique() for col in df.columns]
# Generate all possible combinations across columns
combinations = list(itertools.product(*columns_data))
# Create a new DataFrame from the combinations and write to CSV
output_df = pd.DataFrame(combinations, columns=df.columns)
output_df.to_csv(output_file, index=False)
print(f"Combinations have been saved to {output_file}")
# Usage example:
generate_combinations('input.csv', 'output.csv')
chat1
import polars as pl
def flag_offline_periods(df):
# Count entries per bucket
counts = df.groupby('timestamp_bucket').agg(pl.count('*').alias('count'))
# Identify buckets with more than 20 entries
high_count_buckets = counts.filter(pl.col('count') > 20)['timestamp_bucket']
# Create a function to generate offline periods
def generate_offline_periods(high_count_buckets):
offline_periods = []
for bucket in high_count_buckets:
offline_end = bucket + pl.duration(seconds=30)
offline_periods.append((bucket, offline_end))
return offline_periods
# Generate offline periods
offline_periods = generate_offline_periods(high_count_buckets)
# Merge overlapping periods
merged_periods = []
for start, end in sorted(offline_periods):
if merged_periods and start <= merged_periods[-1][1]:
merged_periods[-1] = (merged_periods[-1][0], max(merged_periods[-1][1], end))
else:
merged_periods.append((start, end))
# Create a function to check if a timestamp is within offline periods
def is_offline(timestamp):
return any(start <= timestamp < end for start, end in merged_periods)
# Apply the offline check to each row
df = df.with_columns(
pl.when(pl.col('timestamp_bucket').apply(is_offline))
.then(pl.lit('OFFLINE'))
.otherwise(pl.lit('ONLINE'))
.alias('status')
)
return df
# Assuming your DataFrame is called 'df'
df = flag_offline_periods(df)