Pandas

• pd.read_csv: read data from csv

Overview

• df.head(): first few data
• df.info(): how many entries, what are the columns, dtypes
• df.describe(): some statistics of each column
• df.shape: shape
• df.value_counts(): amount of data for each value in a column

Select

• iloc[] selects by integer position
• loc[] selects by label
• df.isna() finds N/A

mask = (penguins["body_mass_g"] > 5000) & (penguins["flipper_length_mm"] > 220)
penguins[mask].head()

• df.dropna(): drop rows includes N/A

Grouping

• df.groupby(): group data by the value in a column

df.groupby("species")[["bill_length_mm", "flipper_length_mm", "body_mass_g"]].mean().round(1)

Numpy

• df.to_numpy() turns a Dataframe to a numpy array.