import grizzlars as gl

df = gl.DataFrame({
  "symbol": ["AAPL", "GOOGL", "MSFT", "AMZN", "META"],
  "price":  [189.3,  175.1,   415.2,  185.0,  502.7],
  "volume": [52_000_000, 18_000_000, 22_000_000, 31_000_000, 14_000_000],
  "active": [True, True, True, False, True],
})

print(df)
# Load from CSV
df = gl.read_csv("prices.csv")

df = gl.read_csv("data.csv")

# Promote a column to the index
df = gl.read_csv("data.csv", index_col="Id")

# Force a column to a specific type (triggers slower Python fallback)
df = gl.read_csv("data.csv", dtype={"code": str})

df.to_csv("output.csv")
df.to_csv("output.csv", index=False)  # omit index column

df = gl.DataFrame({
  "x": [1, 2, 3],
  "y": [4.0, 5.0, 6.0],
})

# Custom index
df = gl.DataFrame({"x": [10, 20, 30]}, index=[100, 200, 300])

df.shape          # (rows, cols) — tuple
len(df)           # row count
df.columns        # list of column names
df.index          # numpy uint64 array of index values
df.dtypes()       # {"col": "double" | "int64" | "bool" | "string", ...}

# Read a column — returns numpy array (numeric/bool) or list (string)
prices = df["price"]

# Add or overwrite a column in-place
df["log_price"] = np.log(df["price"])
df["label"] = ["cheap", "expensive", "mid"]

# Check membership
"price" in df   # True / False

# Non-mutating variants
df2 = df.with_column("log_price", np.log(df["price"]))
df2 = df.assign(log_price=np.log(df["price"]), rank=[1, 2, 3])

# Select a subset of columns
df2 = df.select(["symbol", "price"])

# Rename columns in-place
df.rename({"symbol": "ticker", "price": "close"})

# Drop a column in-place
df.drop("log_price")

df.head(10)          # first 10 rows
df.tail(10)          # last 10 rows

df.iloc[0]           # single row as DataFrame
df.iloc[10:50]       # slice (step=1 only)
df.iloc[-1]          # last row

# Mask mode (recommended — compose with numpy operators)
cheap = df.filter(df["price"] < 200)
active = df.filter(df["active"] == True)

# String operator mode
cheap = df.filter("price", "<", 200)
# Operators: ">" ">=" "<" "<=" "==" "!="

# Combine conditions
mask = (df["price"] < 200) & (df["volume"] > 10_000_000)
df.filter(mask)

# len() and shape are free (no materialisation)
print(len(cheap))     # instant
print(cheap.shape)    # instant

# Materialises on first real operation
print(cheap["symbol"])
cheap.sort("price")

df.sort("price")                       # ascending
df.sort("price", ascending=False)      # descending
df.sort_values("volume", ascending=False)  # alias for sort()
df.sort_index()                        # sort by index ascending
df.sort_index(ascending=False)         # sort by index descending

df.mean("price")         # arithmetic mean
df.std("price")          # sample standard deviation (n-1)
df.sum("price")          # total
df.min("price")          # minimum value
df.max("price")          # maximum value
df.count("price")        # non-null count

df.quantile("price", 0.5)    # median (q in [0, 1])
df.corr("price", "volume")   # Pearson correlation
df.cov("price", "volume")    # sample covariance

df.nunique("symbol")         # number of distinct values
df.unique("symbol")          # sorted array of distinct values
df.n_missing("price")        # count of NaN / empty-string values

# Frequency table — returns DataFrame with ["value", "count"]
df.value_counts("symbol")

stats = df.describe()
# statistic  |  price  |  volume
# -----------+---------+---------
# count      |  5.0    |  5.0
# mean       |  ...    |  ...
# std        |  ...    |  ...
# min        |  ...    |  ...
# max        |  ...    |  ...
# sum        |  ...    |  ...

# agg() accepts a dict of {column: function}
# Functions: "mean", "sum", "min", "max", "count", "std"
result = df.groupby("sector").agg({"price": "mean", "volume": "sum"})

# Shorthand methods
df.groupby("sector").mean("price")
df.groupby("sector").sum("volume")
df.groupby("sector").min("price")
df.groupby("sector").max("price")
df.groupby("sector").count("price")
df.groupby("sector").std("price")

left  = gl.read_csv("orders.csv",   index_col="order_id")
right = gl.read_csv("products.csv", index_col="order_id")

inner  = left.join(right, how="inner")   # default
left_j = left.join(right, how="left")    # unmatched right → NaN / ""
right_j = left.join(right, how="right")
outer  = left.join(right, how="outer")

combined = df_a.concat(df_b)

# Stack many frames
from functools import reduce
all_data = reduce(lambda a, b: a.concat(b), frames)

df.rolling_mean("price", window=20)   # 20-period moving average
df.rolling_sum("volume", window=5)
df.rolling_std("price", window=20)
df.rolling_min("price", window=10)
df.rolling_max("price", window=10)

# Generic form
df.rolling("price", window=20, func="mean")
# func: "mean" | "sum" | "std" | "min" | "max"

df.cumsum("volume")    # cumulative sum
df.cumprod("factor")   # cumulative product
df.cummin("price")     # running minimum
df.cummax("price")     # running maximum

df.shift("price", n=1)    # lag by 1 period; NaN at boundary
df.shift("price", n=-1)   # lead by 1 period
df.pct_change("price")    # (price[i] - price[i-1]) / price[i-1]; first element NaN

# Remove rows with duplicate values in a column (keep first)
df.drop_duplicates("symbol")

# Remove rows where a column is NaN or empty string
df.drop_na("price")

# Fill NaN / empty values in-place (returns self)
df.fillna("price", 0.0)
df.fillna("label", "unknown")

import grizzlars as gl

gl.set_optimum_thread_level()   # auto-detect (called on import)
gl.set_thread_level(4)          # pin to 4 threads
gl.get_thread_level()           # returns current thread count