Pandas Date and Time Series Functionality
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What does pd.Timestamp('2017-01-01').year return?

  • 2017-01-01
  • 2017 (correct)
  • 1
  • Timestamp('2017-01-01 00:00:00')

The default frequency of a pd.Period without specifying is daily.

False (B)

What is the output of pd.Period('2017-01').asfreq('D')?

Period('2017-01-31', 'D')

The method used to convert a pd.Period into pd.Timestamp is called _________.

<p>to_timestamp</p> Signup and view all the answers

Match the following Pandas functions with their descriptions:

<p>pd.Timestamp() = Represents a specific point in time. pd.Period() = Represents a time span. pd.date_range() = Generates a sequence of dates. pd.to_datetime() = Converts arguments to datetime.</p> Signup and view all the answers

What is the data type of the index created by pd.date_range(start='2017-1-1', periods=12, freq='M')?

<p>datetime64[ns] (C)</p> Signup and view all the answers

pd.Timestamp('2017-01-01') == pd.Timestamp(datetime(2017, 1, 1)) returns True.

<p>True (A)</p> Signup and view all the answers

What is the frequency of the index created by pd.date_range(start='2017-1-1', periods=12, freq='M')?

<p>M</p> Signup and view all the answers

The function _______ is used to create a sequence of dates.

<p>pd.date_range</p> Signup and view all the answers

If index is a DatetimeIndex, what data type would pd.DataFrame({'data': index})['data'] be?

<p>datetime64[ns] (D)</p> Signup and view all the answers

What does the parse_dates=['date'] argument in the pd.read_csv() function do?

<p>It converts the 'date' column to datetime format. (B)</p> Signup and view all the answers

The .shift() function with no arguments will shift the data by one period forward.

<p>False (B)</p> Signup and view all the answers

What is the default value for the periods parameter in the .shift() function?

<p>1</p> Signup and view all the answers

The .diff() function calculates the difference between the current and the immediately preceding row, specifically: xt - ______

<p>xt-1</p> Signup and view all the answers

What does the pct_change() function calculate?

<p>The percentage change between the current and a prior element. (A)</p> Signup and view all the answers

If you use shift(periods=-1), does the first or last value end up missing?

<p>last</p> Signup and view all the answers

The .mul() method is used to perform subtraction.

<p>False (B)</p> Signup and view all the answers

Match the pandas operations with their descriptions:

<p>.shift() = Moves data by a specified number of periods. .pct_change() = Calculates the percentage change between the current and a prior element. .diff() = Calculates the difference between a current and previous element. .div() = Performs division between elements of a column or series.</p> Signup and view all the answers

When the index_col argument is used in pd.read_csv(), it sets the specified column as the ______ of the DataFrame.

<p>index</p> Signup and view all the answers

What is the result of google.price.shift() if applied to a 'price' column with values [10, 20, 30, 40]?

<p>[NaN, 10, 20, 30] (A)</p> Signup and view all the answers

The result of google.price.pct_change().mul(100) is the same as google.price.return.

<p>True (A)</p> Signup and view all the answers

What does google['change'] = google.price.div(google.shifted) calculate?

<p>The ratio of price to shifted. (A)</p> Signup and view all the answers

What is the data type of the 'date' column in the original Google DataFrame before any conversions?

<p>object (A)</p> Signup and view all the answers

What does the google.info() method generally display about a pandas DataFrame?

<p>Information about the DataFrame, including the data types and number of non-null values of each column.</p> Signup and view all the answers

The google.set_index('date', inplace=True) operation modifies the original DataFrame.

<p>True (A)</p> Signup and view all the answers

The .sub() method is used for ______.

<p>subtraction</p> Signup and view all the answers

Which method is used to calculate the percentage change over a specific number of periods?

<p>pct_change(periods=) (D)</p> Signup and view all the answers

After setting 'date' as the index, how many columns are present in the Google DataFrame?

<p>1</p> Signup and view all the answers

To access the 'price' on the 1st of June, 2016 using .loc[], you would use the syntax google.loc['2016-6-1', '______'].

<p>price</p> Signup and view all the answers

Which method is used to convert the 'date' column to datetime objects?

<p>pd.to_datetime(google.date) (D)</p> Signup and view all the answers

The code google['2015'].info() returns information about the DataFrame containing all the data for the entire year 2015.

<p>True (A)</p> Signup and view all the answers

What is the frequency of the DateTimeIndex after using google = google.asfreq('B')?

<p>B</p> Signup and view all the answers

The asfreq('D') method is used to set the calendar ______ frequency

<p>day</p> Signup and view all the answers

What is the resulting data type of the 'date' column in the Google DataFrame, after converting it using 'pd.to_datetime'?

<p>datetime64[ns] (B)</p> Signup and view all the answers

The .info() method on a DataFrame displays the first few rows of the data.

<p>False (B)</p> Signup and view all the answers

What does plt.tight_layout() do in the given code?

<p>Adjust plot parameters for a tight layout</p> Signup and view all the answers

The .plot() method with the argument title='Google Stock Price' will plot the Google stock prices and set the title to ______.

<p>Google Stock Price</p> Signup and view all the answers

Match the code snippets with their descriptions:

<p>google.info() = Displays DataFrame information google.head() = Displays first few rows of the DataFrame google.price.plot() = Plots the 'price' column google.set_index('date', inplace=True) = Sets the 'date' column as the DataFrame index</p> Signup and view all the answers

How many data points are in the Google DataFrame between '2015-03' and '2016-02' using the slice method?

<p>252 (D)</p> Signup and view all the answers

The command google[google.price.isnull()] will return only the rows of the DataFrame where the price is missing.

<p>True (A)</p> Signup and view all the answers

Flashcards

Timestamp

A pandas object representing a specific date and time.

Period

Represents time intervals or periods with a specific frequency (e.g., month, day, year).

DatetimeIndex

A pandas object that represents a sequence of dates with a defined frequency. It is used as an index for data.

PeriodIndex

A pandas object that represents a sequence of time periods with a defined frequency. It is used for organizing data by time.

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pd.date_range

Create a series of timestamps with regular intervals for data analysis.

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pd.to_datetime

Function for converting strings or other formats into datetime objects.

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pd.DataFrame(data, index=date_range)

Create a pandas DataFrame, where the index is a DatetimeIndex.

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Timestamp.to_period()

Convert Timestamps to Periods.

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Period.to_timestamp()

Convert Periods to Timestamps.

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Period.asfreq

Convert Periods to another frequency.

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What is the Pandas .info() method?

The .info() method in Pandas provides a concise summary of a DataFrame's structure. It displays the number of rows, columns, data types, and non-null values.

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What is a DatetimeIndex?

A DatetimeIndex in Pandas is used to index data based on dates and times, allowing for efficient time-series operations.

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What does google.set_index('date', inplace=True) do?

.set_index('date', inplace=True) is used in Pandas to set the 'date' column as the index of a DataFrame, in place, modifying the original DataFrame directly.

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How do you select data by labels in Pandas?

.loc[] is used in Pandas for label-based indexing, allowing you to access data by row and column labels. For example, google.loc['2016-6-1', 'price'] retrieves the stock price on June 1st, 2016.

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How can you access a range of records based on partial date information?

You can use slicing with single quotation marks to select a range of rows based on partial date information. For example, google['2015'] extracts data for all records within the year 2015.

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When slicing with a colon, does the final date get included?

Slicing with a colon (:) in Pandas allows for selecting a range of rows based on date intervals, including the end date. For example, google['2015-3': '2016-2'] includes data from March 2015 to February 2016.

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What does .asfreq('D') do?

.asfreq('D') in Pandas creates a DataFrame where each day in the specified range is represented, filling missing values with 'NaN' (Not a Number). It allows for daily frequency analysis.

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How do you identify missing values in a Pandas DataFrame?

.isnull() in Pandas is used to identify missing values or 'NaN' (Not a Number) within a DataFrame. google[google.price.isnull()] selects rows where the 'price' value is missing.

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What does .asfreq('B') do?

.asfreq('B') in Pandas resamples data to a business-day frequency, removing non-business days (weekends and holidays). This is useful for financial data analysis.

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pd.read_csv()

A function used to read data from a CSV (Comma Separated Values) file into a Pandas DataFrame. It takes the file path as input and can also be used to parse dates and set an index column.

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Google Stock Price on a given Date

It represents the price of a stock on a specific date, which is represented by the index.

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Setting the Index Column

The 'date' column is converted into datetime objects and set as the index for the DataFrame, making it easier to work with time-series data.

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google.info()

A method used to display information about the DataFrame, including data types and how many non-null values are present in each column.

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Series/DataFrame.shift()

A method used to shift the values in a series or DataFrame, creating a copy of the original series with values moved to the next or previous positions. Default period is 1.

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Adding Shifted Price Column

A new column called 'shifted' is added to the DataFrame. This column contains the price values shifted one period back using the 'shift' method.

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Price and Shifted Price

In this context, 'shifted' represents the previous day's price, while 'price' represents the current day's price.

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Series/DataFrame.pct_change()

A method used to calculate the percentage change between consecutive values in a series or DataFrame.

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Series/DataFrame.pct_change(periods=n)

Calculating the percentage change between consecutive values in a series or DataFrame, but with the option to specify a different period. 'periods=3' would calculate the change from 3 periods ago.

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Adding Daily Percentage Change Column

A new column called 'pct_change' is added to the DataFrame. This column shows the daily percentage change in the stock price compared to the previous trading day.

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Series/DataFrame.diff()

This method creates a new column with the difference in price between each period.

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Calculating Daily Return

This method creates a new column with the daily return as a percentage calculated by dividing 'price' by its previous value (shifted), then subtracting 1 and multiplying by 100.

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Calculating 3-day Return

This method creates a new column with the daily return as a percentage calculated as the percentage change over 3 periods.

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Study Notes

Date and Time Series Functionality in Pandas

  • Pandas provides objects for points in time and periods, with attributes and methods for time-related details.
  • Time series data can be stored in Pandas Series or DataFrame columns, with the index often used to convert objects into time series.

Basic Building Block: pd.Timestamp

  • pd.Timestamp creates a timestamp object.
  • Dates can be represented as strings or datetime objects.
  • Timestamp objects provide attributes for specific time information (e.g., year, day_name).

More Building Blocks: pd.Period and Frequency

  • pd.Period represents a period of time (e.g., a month).
  • freq attribute stores the frequency information (e.g., monthly, daily).
  • Period objects can be converted to Timestamp objects and vice-versa, enabling date arithmetic.

Sequences of Dates and Times

  • pd.date_range creates a sequence of dates with specified start, end, number of periods, or frequency.
  • pd.date_range returns a DatetimeIndex, which is a sequence of Timestamp objects with frequency information.
  • Time series data can be created as a DataFrame using the DatetimeIndex as the index..

Frequency Aliases and Time Information

  • Common frequency aliases exist for different time units (e.g., 'H' for hours, 'D' for days, 'M' for months, 'Q' for quarters, 'A' for years).
  • Timestamps have attributes to access various time components like second, minute, hour, day,, month,weekday, weekofyear, and dayofyear.

Indexing and Resampling Time Series

  • String dates can be parsed and converted to datetime64 using pd.to_datetime.
  • DataFrames can be indexed using date strings or intervals, for example, google['2015-3':'2016-2'].
  • set_index sets the date as the DataFrame index.
  • asfreq adjusts the frequency of the index.
  • Upsampling creates additional data points while downsampling reduces data points to a coarser frequency.

Lags, Changes, and Returns for Stock Price Series

  • shift moves data forward or backward in time.
  • Calculating changes in values over time (e.g., daily percentage changes) is possible through Pandas methods.
  • Built-in methods like .diff() and .pct_change() can be used to calculate changes in values over periods.
  • Multi-period returns can be calculated using pct_change() with period argument .

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