Pandas Data Manipulation

Filter by condition
df[df["age"] > 25]

Multiple conditions
df[(df["age"] > 25) & (df["city"] == "NYC")]

Filter by list
df[df["name"].isin(["John", "Jane"])]

Check for nulls
df.isnull()
df.isnull().sum()

Drop missing values
df.dropna()
df.dropna(subset=["column"])

Fill missing values
df.fillna(0)
df["age"].fillna(df["age"].mean())

Group by
df.groupby("city").mean()
df.groupby("city")["age"].sum()

Multiple aggregations
df.groupby("city").agg({
  "age": ["mean", "max"],
  "salary": "sum"
})

Merge DataFrames
pd.merge(df1, df2, on="id")

Different join types
pd.merge(df1, df2, how="left")
pd.merge(df1, df2, how="outer")

Concatenate
pd.concat([df1, df2])
pd.concat([df1, df2], axis=1)