15.1: Dataframe basics
- Page ID
- 24175
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)In the realm of data analysis, the pandas DataFrame is a cornerstone structure, a tabular arrangement akin to a spreadsheet or SQL table. A DataFrame essentially consists of multiple pandas Series that share a common index. For illustration, consider a gradebook where the index represents the names of the students, and the columns denote various assignments. Each column is, in essence, a pandas Series, compiled together under a single DataFrame.
The index is the column of names and the other columns are grades for an assignment.
Creating a DataFrame can be done in numerous ways. For example, I generated the DataFrame above with this code:
names = ['Alice', 'Bob', 'Charlie', 'David', 'Emily', 'Frank', 'Grace', 'Helen', 'Isaac', 'Jill'] # Define the column names columns = ['Quiz1', 'Quiz2', 'Quiz3', 'Homework1', 'Homework2', 'Exam1', 'Exam2'] # Generate random integers between 70 and 100 for the values values = np.random.randint(70, 101, size=(len(names), len(columns))) # Create the DataFrame df = pd.DataFrame(values, index=names, columns=columns)
Alternatively, let's say you have two Series, each representing scores from different exams, "Exam1" and "Exam2." The index for each Series contains the names of the students.
Here's how you could create these Series:
# Create two pandas Series with names as the index exam1 = pd.Series([85, 90, 78, 92], index=['Alice', 'Bob', 'Charlie', 'David']) exam2 = pd.Series([88, 84, 76, 95], index=['Alice', 'Bob', 'Charlie', 'David'])
# Concatenate Series into a DataFrame df = pd.concat([exam1, exam2], axis=1, keys=['Exam1', 'Exam2'])
df is now:
Let's talk about the "axis=1" keyword in the concat() command. In the pandas library, the "axis" determines the direction in which an operation is performed. This is a fundamental concept that not only applies to concatenation but also to other operations like summation, mean calculation, and data manipulation. The axis parameter can essentially take on one of two values: `0` or `1`.
With `axis=1`, the columns are placed side by side, as in the example above.
If axis=0, then the Series would be concatenated end-to-end, into one long column:
