Python for Data Analysis Questions

import pandas as pd
from collections import defaultdict

def top_n_customers_by_revenue(csv_path, chunksize=10_000_00, n=10):
    # running accumulator: customer_id -> revenue
    acc = defaultdict(float)
    cols = ['customer_id', 'revenue']  # adjust to real column names

    for chunk in pd.read_csv(csv_path, usecols=cols, chunksize=chunksize):
        # clean / cast if needed
        chunk = chunk.dropna(subset=['customer_id', 'revenue'])
        chunk['revenue'] = pd.to_numeric(chunk['revenue'], errors='coerce').fillna(0)

        # per-chunk aggregation
        grp = chunk.groupby('customer_id', sort=False)['revenue'].sum()

        # incremental accumulate into acc
        for cid, rev in grp.items():
            acc[cid] += rev

    # convert accumulator to DataFrame and get top N
    result = (pd.DataFrame.from_dict(acc, orient='index', columns=['revenue'])
                .rename_axis('customer_id')
                .reset_index()
                .sort_values('revenue', ascending=False)
                .head(n))
    return result

# Example usage:
# df_top10 = top_n_customers_by_revenue('large_sales.csv', chunksize=500_000, n=10)
# print(df_top10)

import pandas as pd
import numpy as np

def clean_reporting_table(df, numeric_strategy='median', cat_fill=None, drop_pct=0.5):
    """
    df: input DataFrame
    numeric_strategy: 'mean', 'median', or dict of {col: value}
    cat_fill: None (leave), 'Unknown', or dict
    drop_pct: fraction of columns null above which rows are removed
    Returns: missing_summary, cleaned_df, removed_rows
    """
    total = len(df)
    missing_summary = (df.isna().sum().rename('missing_count')
                       .to_frame()
                       .assign(missing_pct=lambda x: x['missing_count']/len(df)))
    
    # Flag rows to remove (> drop_pct null columns)
    row_null_frac = df.isna().mean(axis=1)
    to_remove = row_null_frac > drop_pct
    removed_rows = df[to_remove].copy().assign(_removed_reason='>50% null')
    
    # Keep only rows not removed
    cleaned = df[~to_remove].copy()
    
    # Impute numeric columns
    num_cols = cleaned.select_dtypes(include=[np.number]).columns
    if isinstance(numeric_strategy, dict):
        for c in num_cols:
            if c in numeric_strategy:
                cleaned[c] = cleaned[c].fillna(numeric_strategy[c])
    else:
        for c in num_cols:
            if cleaned[c].isna().any():
                if numeric_strategy == 'mean':
                    val = cleaned[c].mean()
                else:
                    val = cleaned[c].median()
                cleaned[c] = cleaned[c].fillna(val)
    
    # Handle categorical columns
    cat_cols = cleaned.select_dtypes(include=['object','category']).columns
    if isinstance(cat_fill, dict):
        for c in cat_cols:
            if c in cat_fill:
                cleaned[c] = cleaned[c].fillna(cat_fill[c])
    elif cat_fill == 'Unknown':
        cleaned[cat_cols] = cleaned[cat_cols].fillna('Unknown')
    # else leave categorical nulls for BI to display as blanks
    
    return missing_summary, cleaned, removed_rows

# Phase 1: Partition both tables to disk by hash(key)
def partition_to_disk(table_path, out_dir, num_partitions):
    for row in stream_rows(table_path):
        p = hash(row.key) % num_partitions
        append_row(out_dir + f"/part_{p}.csv", row)

# Phase 2: For each partition, load smaller side into memory and probe
def hash_partitioned_join(left_path, right_path, out_path, num_partitions, mem_limit):
    partition_to_disk(left_path, "/tmp/left_parts", num_partitions)
    partition_to_disk(right_path, "/tmp/right_parts", num_partitions)
    for p in range(num_partitions):
        left_size = file_size("/tmp/left_parts/part_{p}.csv")
        right_size = file_size("/tmp/right_parts/part_{p}.csv")
        # choose smaller partition to build hash table
        build_path, probe_path = (left,right) if left_size < right_size else (right,left)
        ht = {}
        for row in stream_rows(build_path):
            ht.setdefault(row.key, []).append(row)
        with open(out_path, "a") as out:
            for prow in stream_rows(probe_path):
                for brow in ht.get(prow.key, []):
                    out.write(join_rows(brow, prow))

import pandas as pd

# Example inputs
a = pd.DataFrame({'id_int':[1,2,3,3],'metric':[10,20,30,30]})
b = pd.DataFrame({'id_str':['001','002','003'],'attr':['x','y','z']})
c = pd.DataFrame({'code_padded':['0001','0002','0004'],'flag':[True,False,True]})

# Canonicalization function: integer-like -> zero-padded string of width 4
def normalize_key(val):
    if pd.isna(val):
        return None
    s = str(val).strip()
    # remove non-digit padding like leading/trailing spaces, possible prefixes
    s = ''.join(ch for ch in s if ch.isdigit())
    if s == '':
        return None
    return s.zfill(4)

for df, col in [(a,'id_int'), (b,'id_str'), (c,'code_padded')]:
    df['key'] = df[col].apply(normalize_key)

# Deduplicate keeping most recent / first occurrence depending on business rule
a = a.drop_duplicates('key', keep='first')
b = b.drop_duplicates('key', keep='first')
c = c.drop_duplicates('key', keep='first')

# Perform joins: start from the primary dataset used by dashboard (assume 'a')
joined = a.merge(b[['key','attr']], on='key', how='left')\
          .merge(c[['key','flag']], on='key', how='left')

# Referential integrity checks
keys_a = set(a['key'].dropna())
keys_b = set(b['key'].dropna())
keys_c = set(c['key'].dropna())

missing_in_b = keys_a - keys_b
missing_in_c = keys_a - keys_c

assert len(missing_in_b) == 0 and len(missing_in_c) == 0, "Referential integrity failed"

# If assert fails, handle below (see plan)