Lesson 12

Numeric Indexing of DataFrames

This lesson covers:

  • Accessing specific elements in DataFrames using numeric indices

Accessing elements in a DataFrame is a common task. To begin this lesson, clear the workspace set up some vectors and a $5\times5$ array. These vectors and matrix will make it easy to determine which elements are selected by a command.

Begin by creating:

  • A 5-by-5 DataFrame x_df containing np.arange(25).reshape((5,5)).
  • A 5-element Series y_s containing np.arange(5).
  • A 5-by-5 DataFrame x_named that is x_df with columns "c0", "c1", ..., "c4" and rows "r0", "r1", ..., "r4".
  • A 5-element Series y_named with index "r0", "r1", ..., "r4".
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Problem: Picking an Element out of a DataFrame

Using double index notation, select the (0,2) and the (2,0) element of x_named.

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Problem: Select Elements from Series

Select the 2nd element of y_named.

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Problem: Selecting Rows as Series

Select the 2nd row of x_named using the colon (:) operator.

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Problem: Selecting Rows as DataFrames

  1. Select the 2nd row of x_named using a slice so that the selection remains a DataFrame.
  2. Repeat using a list of indices to retain the DataFrame.
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Problem: Selecting Entire Columns as Series

Select the 2nd column of x_named using the colon (:) operator.

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Problem: Selecting Single Columns as DataFrames

Select the 2nd column of x_named so that the selection remains a DataFrame.

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Problem: Selecting Specific Columns

Select the 2nd and 3rd columns of x_named using a slice.

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Problem: Select Specific Rows

Select the 2nd and 4th rows of x_named using a slice. Repeat the selection using a list of integers.

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Problem: Select arbitrary rows and columns

Combine the previous selections to the 2nd and 3rd columns and the 2nd and 4th rows of x_named.

Note: This is the only important difference with NumPy. Arbitrary row/column selection using DataFrame.iloc is simpler but less flexible.

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Problem: Mixed selection

Select the columns c1 and c2 and the 1st, 3rd and 5th row.

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Problem: Mixed selection 2

Select the rows r1 and r2 and the 1st, 3rd and final column.

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Exercises

Exercise: Select fixed length block

Compute the mean return of the momentum data in the first 66 observations and the last 66 observations.

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# Setup: Load the momentum data

import pandas as pd

momentum = pd.read_csv("data/momentum.csv", index_col="date", parse_dates=True)
momentum.head()
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Exercise: Compute values using fraction of sample

Compute the correlation of momentum portfolio 1, 5, and 10 in the first half of the sample and in the second half.

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