What is a Data Frame?

Table of data
Rows represent observations
Columns (attributes) must have the same data type
Cells in the table can be referenced by intrinsic location (iloc) or by row and column labels (indices)

read_csv()
- Quite customizable
- Has defaults on how data are read in, which can be overridden (dtype parameter)
Other options:
- read_html(), read_excel(),…

Time	Topic
0:45	What is a Data Frame?
3:10	Dataframe as a list of lists
5:20	Dataframe as a collection of dictionaries
10:20	Loading data into a dataframe (`read_csv()`)
14:30	Exploring your data - revealing column data types
16:35	Specifying data types when importing with `read_csv()`
18:06	Specifying a column to be the index when importing with `read_csv()`

Time	Topic
0:20	Inspecting the data with `head()`, `tail()` and `sample()`
1:45	Reading in raw text files stored on the internet; more on `read_csv()`
4:45	Revealing aspects of your dataframe: `len(df)`, `df.shape`, `df.size`
6:42	Listing columns of your dataframe with `df.columns`
8:05	Listing the index of your dataframe with `df.index`
8:55	Setting the index column when reading in your data with `read_csv()`
10:35	Listing data frame info with `df.info()`
13:15	Selecting specific columns from your data frame into a new dataframe
16:15	Selecting a single column into a Series object; what is a Series object?
17:20	Referring to columns: brackets (`df['column']`) vs dot notation (`df.column`)
19:21	Descriptive statistics for a column of data
20:39	Quantiles
21:25	Correlations among numeric columns
22:58	Styling your correlation output
24:47	Generating summary stats with `df.describe()`
25:25	Listing unique values and number of unique values with `df.unique()` and `df.nunique()`
26:52	Listing number of records for each value with `value_counts()`
28:12	Basic plots in Pandas: histograms with df.hist()
30:32	Boxplots