Pandas DataFrames

What Is a DataFrame?

A DataFrame organizes data into rows and columns, much like a spreadsheet or SQL table. Each column can hold a different data type, rows are identified by an index, and columns are identified by labels. Under the hood, every column of a DataFrame is a pandas Series.

Creating a DataFrame

The most common way to build a DataFrame is from a dictionary of equal-length lists, where each key becomes a column name and each list becomes that column's values.

• From a dictionary of lists - each key becomes a column
• From a list of dictionaries - each dictionary becomes a row
• From an existing Series or group of Series
• From a 2-D NumPy array with explicit column names
• From external files such as CSV or JSON, covered in later lessons

import pandas as pd

data = {
    "calories": [420, 380, 390],
    "duration": [50, 40, 45]
}
df = pd.DataFrame(data)
print(df)

Locating Rows With loc

import pandas as pd

data = {"calories": [420, 380, 390], "duration": [50, 40, 45]}
df = pd.DataFrame(data)

print(df.loc[0])
print(df.loc[[0, 1]])

By default, rows are labeled with an automatic integer index starting at 0. You can replace that with meaningful labels of your own, and then use those same labels with loc to select rows.

import pandas as pd

data = {"calories": [420, 380, 390], "duration": [50, 40, 45]}
df = pd.DataFrame(data, index=["day1", "day2", "day3"])
print(df)
print(df.loc["day2"])