Python and pandas cheat sheet

import pandas as pd
import numpy as np
df = pd.read_csv("file.csv", parse_dates=["date"])
df = pd.read_excel("file.xlsx", sheet_name="Data")
df = pd.read_parquet("file.parquet")
df.to_csv("out.csv", index=False)
df.to_excel("out.xlsx", index=False)
df.to_parquet("out.parquet")

df.head(10); df.tail()
df.shape            # (rows, columns)
df.info()           # types and nulls
df.describe()       # numeric stats
df["col"].value_counts()
df.isna().sum()     # nulls per column

df["col"]                    # one column (Series)
df[["a", "b"]]               # several columns
df.loc[rows, "col"]          # by label
df.iloc[0:5, 0:3]            # by position

df[df["amount"] > 100]
df[(df["region"] == "North") & (df["amount"] > 100)]
df[df["region"].isin(["North", "West"])]
df[df["name"].str.contains("ltd", case=False)]
df.query("amount > 100 and region == 'North'")

df["revenue"] = df["qty"] * df["price"]
df = df.assign(vat=lambda d: d["revenue"] * 0.2)
df["size"] = np.where(df["revenue"] > 500, "Large", "Small")
df["name"] = df["name"].str.strip().str.title()

(df.groupby("region", as_index=False)
   .agg(revenue=("revenue", "sum"),
        orders=("order_id", "nunique"),
        avg=("revenue", "mean")))

df.merge(products, on="product_id", how="left",
         validate="many_to_one")
pd.concat([df1, df2], ignore_index=True)  # stack

pd.pivot_table(df, index="region", columns="month",
               values="revenue", aggfunc="sum",
               fill_value=0, margins=True)
df.melt(id_vars="region", var_name="month",
        value_name="revenue")          # wide to long

df["date"] = pd.to_datetime(df["date"], dayfirst=True)
df["month"] = df["date"].dt.to_period("M")
df["weekday"] = df["date"].dt.day_name()
df.set_index("date").resample("W")["revenue"].sum()

df.dropna(subset=["customer_id"])
df["discount"] = df["discount"].fillna(0)
df.drop_duplicates(subset="order_id", keep="last")
df["order_id"].duplicated().sum()

df.sort_values(["region", "revenue"], ascending=[True, False])
df["rank"] = df.groupby("region")["revenue"].rank(ascending=False)
df["running"] = df.groupby("customer")["amount"].cumsum()
df["prev"] = df.groupby("customer")["amount"].shift(1)
df["ma7"] = df["amount"].rolling(7).mean()

report = (
    pd.read_csv("sales.csv", parse_dates=["date"])
      .query("status == 'completed'")
      .assign(month=lambda d: d["date"].dt.to_period("M"))
      .groupby("month", as_index=False)["amount"].sum()
)