Create DataFrames and Series

Matter Transformation

Did you know that your spaceship is equipped with a powerful matter transformer? The matter transformer converts objects into other materials. Matter transformation is quite useful to convert Python data structures like lists and dictionaries into pandas types like Series and DataFramse.

However, most of the time your crew uses the machine to make ice cream.

Below you find a few code examples.

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Series from a Python List

Every element of a Python list becomes a position in the Series. The index parameter is optional:

import pandas as pd

s = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])

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Series from a NumPy array

NumPy arrays convert effortlessly, because pandas uses them under the hood anyway. This is especially useful to fill columns or DataFrames with random numbers.

import numpy as np

r = np.random(1, 10, size=10)
s = pd.Series(r)

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DataFrame from a nested list

Nested (two-dimensional) lists convert easily to a DataFrame. The only thing you should add are the column labels unless you want to have integer numbers.

penguins = pd.DataFrame(
    [["Skipper", 12],
     ["Kowalski", 34],
     ["Rico", 56],
     ["Private", 78]],
    columns=["name", "id"])

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DataFrame from a dictionary

When using a dictionary, the keys are used as column names, the values are lists for each column. Not that the lists have to be of equal length.

penguins = pd.DataFrame({
    "name": ["Skipper", "Kowalski", "Rico", "Private"],
    "id": [12, 34, 56, 78]
})

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DataFrame from a Numpy array

Numpy makes it easy to create really huge DataFrames. The index and column names is totally optional, because consecutive numbers are used by default.

data = np.random.normal(size=(100, 100))
pd.DataFrame(data, index=range(100), columns=range(100))