Pandas DataFrames

What is a DataFrame?

A Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns.

Example

Create a simple Pandas DataFrame:

import pandas as pd

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
df = pd.DataFrame(data)

print(df)

   calories  duration
0       420        50
1       380        40
2       390        45

Locate Row

As you can see from the result above, the DataFrame is like a table with rows and columns.

Pandas use the loc attribute to return one or more specified row(s)

Example

Return row 0:

#refer to the row index:
print(df.loc[0])

calories    420
duration     50
Name: 0, dtype: int64

Example

Return row 0 and 1:

#use a list of indexes:
print(df.loc[[0, 1]])

   calories  duration
0       420        50
1       380        40

Named Indexes

With the index argument, you can name your own indexes.

Example

Add a list of names to give each row a name:

import pandas as pd

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

df = pd.DataFrame(data, index = ["day1", "day2", "day3"])

print(df)

      calories  duration
day1       420        50
day2       380        40
day3       390        45

Locate Named Indexes

Use the named index in the loc attribute to return the specified row(s).

Example

Return "day2":

#refer to the named index:
print(df.loc["day2"])

calories    380
duration     40
Name: day2, dtype: int64