- 10 Minutes To Pandas Python
- 10 Minutes To Pandas Youtube
- 10 Minutes To Pandas Pdf
- 10 Minutes To Pandas Answer
The Pandas
map( )
function is used to map each value from a Series object to another value using a dictionary/function/Series. It is a convenience function to map values of a Series from one domain to another domain.10 Minutes to pandas; Or, you can take a look at following video that might be interesting: 10-minute tour of pandas from Wes McKinney on Vimeo.
Let’s have a look at the documentation of the
map
function,- Contribute to iTenki/10-Minutes-to-pandas development by creating an account on GitHub.
- Measure the ears on your cat ear headband. Cut 4 half circles out of black felt that are slightly larger than your cat ears. You can use our template from above as a guide.
- Pythonのサードパーティパッケージであるpandasは、データを扱うための非常に優れたパッケージです。一方で機能が多岐にわたる分、使い方が覚えにくいところもあります。この記事ともう一つの記事ではpandas公式ライブライの「10 minutes to pandas」に沿ってpandasの使い方を解説しています。.
- map is a Series method – operated on top of a Series object.
In the above,
pandas.Series.map
takes one major argument, “arg”.As mentioned in the parameters above, there are 3 different types of possible placeholders for “arg”. In simple they are;
- A Dictionary
- A Function
- An Indexed Series
We’ll explore each of the above argument types in detail. You can use anyone based upon your use-case.
Let’s create a DataFrame that we can use further in the tutorial to explore the map function. The data we have is information about 4 persons;
Each column in the DataFrame is of Series type. So, we can map a dictionary to a column in the DataFrame because the map is a Series method.
From the possible different types of arguments to the map function mentioned above, let’s use the dictionary type in this section. In Machine Learning, the data we provide to create models is always in numerical form. If you observe the “Sex” column’s dtype in the DataFrame below, it’s of String (object) type.
All values of the “Sex” column values are one of the two discrete values – “M” or “F”. “M” representing Male and “F” representing Female. We can’t provide this column to build a Machine Learning model as it’s not of numerical type. So, the use-case is to convert this column to a numerical type. This kind of data is called “Categorical data” in Machine Learning terminology.
We shall use the map function with a dictionary argument to convert the “Sex” column to a numerical data type. This process of converting Categorical data to numerical data is referred to as “Encoding”. As we have only 2 categories this encoding process is called as “Binary Encoding”.
The code for it is,
If you observe the above resultant Series, ‘M’ is mapped to 0 and ‘F’ is mapped to 1 in correspondence to the dictionary.
The above process of mapping using a dictionary can be visualised through the following animated video,
From the possible different types of arguments to the map function mentioned above, let’s use the “Function” type in this section. Let’s achieve the same results of the above dictionary mapping using a Python function.
We need to create a function for it at first. The function should take all values in the “Sex” column one by one and convert them to respective integers.
Now let’s use the above function to map it to the “Sex” column.
The code for it is,
10 Minutes To Pandas Python
The above result is the same as the result of using the dictionary argument. We can check it by comparison;
From the above result, you can see that both results are equal.
![Minutes Minutes](/uploads/1/1/8/5/118526369/826014229.jpeg)
![Pandas Pandas](/uploads/1/1/8/5/118526369/637642659.gif)
The above process of mapping using a function can be visualised through the following animated video,
From the possible different types of arguments to the map function mentioned above, let’s use the “Indexed Series” type in this section. The people in our DataFrame are ready to provide their nicknames to us. Assume that the nicknames are provided in a Series object. We would like to map our “Name” column of the DataFrame to the nicknames. The condition is;
- The index of the nicknames (called) Series should be equal to the “Name” (caller) column values.
Let’s construct the nicknames column below with the above condition,
Let’s map the above created Series to the “Name” column of the Datarame;
The code for it is,
- The major point of observation in applying the map function is – the index of the resultant Series index is equal to the caller index. This is important because we can add the resultant Series to DataFrame as a column.
Let’s add the resultant Series as a “nick_Name” column to the DataFrame,
The above process of mapping using an indexed Series can be visualised through the following animated video,
Every single column in a DataFrame is a Series and the map is a Series method. So, we have seen only mapping a single column in the above sections using the Pandas map function. But there are hacks in Pandas to make the map function work for multiple columns. Multiple columns combined together form a DataFrame. There is a process called stacking in Pandas. “Stacking” creates a Series of Series (columns) from a DataFrame. Here, all the columns of DataFrame are stacked as Series to form another Series.
We have encoded the “M” and “F” values to 0 and 1 in the previous section. When building Machine Learning models, there are chances where 1 is interpreted as greater than 0 in doing calculations. But, here they are 2 different categories and are not comparable.
So, let’s store the data in a different way in our DataFrame. Let’s dedicate separate columns for male (“M”) and female (“F”). And, we can fill in “Yes” and “No” for a person based upon their gender. This introduces the redundancy of the data but solves our discussed problem above.
It can be done so by the following code,
Now, we shall map the 2 columns “Male” and “Female” to numerical values. To do so, we should take the subset of the DataFrame.
You can observe that we have a DataFrame of two columns above. The main point to note is both of the columns have the same set of possible values.
Thereafter, we will use the stacking hack and map two columns to the numerical values. This can be implemented using the following code,
If you observe the above code and results, the DataFrame is first stacked to form a Series. Then the map method is applied to the stacked Series. FInally unstacking it results in, numerical values replaced DataFrame.
In Machine Learning, there are routines to convert a categorical variable column to multiple discrete numerical columns. Such a process of encoding is termed as One-Hot Encoding in Machine Learning terminology.
We have discussed Pandas
apply
function in detail in another tutorial. The map
and apply
functions have some major differences between them. They are;- The first difference is;
map
is only a Series method.apply
is both the Series and DataFrame method.
- The second difference is;
map
takes dict / Series / function as an argumentapply
takes the only function as an argument
- The third difference is;
map
is an element-wise operation on Seriesapply
is used for complex element-wise operations on Series and DataFrame
- The fourth difference is;
map
is used majorly to map values using a dictionaryapply
is used for applying functions that are not available as vectorized aggregation routines on DataFrames
A map function is used majorly to map values of a Series using a dictionary. Whenever you find any categorical data, you can think of a map method to convert them to numerical values. If you liked this tutorial on the
map( )
function and like quiz-based learning, please consider giving it a try to read our Coffee Break Pandas book.Related Posts
Python’s pandas library is frequently used to import, manage, and analyze datasets in a variety of formats. In this article, we’ll use it to analyze Amazon’s stock prices and perform some basic time series operations.
Table of Contents:
- Introduction
- Time series data
- Importing stock data and necessary Python libraries
- Pandas for time series analysis
- Time shifting
Introduction
Stock markets play an important role in the economy of a country. Governments, private sector companies, and central banks keep a close eye on fluctuations in the market as they have much to gain or lose from it. Due to the volatile nature of the stock market, analyzing stock prices is tricky– this is where Python comes in. With built-in tools and external libraries, Python makes the process of analyzing complex stock market data seamless and easy.
Prerequisites
We’ll be analyzing stock data withPython 3, pandas and Matplotlib. To fully benefit from this article, you should be familiar with the basics of pandas as well as the plotting library called Matplotlib.
Time series data
Time series data is a sequence of data points in chronological order that is used by businesses to analyze past data and make future predictions. These data points are a set of observations at specified times and equal intervals, typically with a datetime index and corresponding value. Common examples of time series data in our day-to-day lives include:
- Measuring weather temperatures
- Measuring the number of taxi rides per month
- Predicting a company’s stock prices for the next day
Variations of time series data
- Trend Variation: moves up or down in a reasonably predictable pattern over a long period of time.
- Seasonality Variation: regular and periodic; repeats itself over a specific period, such as a day, week, month, season, etc.
- Cyclical Variation: corresponds with business or economic ‘boom-bust’ cycles, or is cyclical in some other form
- Random Variation: erratic or residual; doesn’t fall under any of the above three classifications.
Here are the four variations of time series data visualized:
Importing stock data and necessary Python libraries
To demonstrate the use of pandas for stock analysis, we will be using Amazon stock prices from 2013 to 2018. We’re pulling the data from Quandl, a company offering a Python API for sourcing a la carte market data. A CSV file of the data in this article can be downloaded from the article’s repository.
Fire up the editor of your choice and type in the following code to import the libraries and data that correspond to this article.
Example code for this article may be found at the Kite Blog repository on Github.
10 Minutes To Pandas Youtube
A first look at Amazon’s stock Prices
Let’s look at the first few columns of the dataset:
Let’s get rid of the first two columns as they don’t add any value to the dataset.
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Let us now look at the datatypes of the various components.
It appears that the Date column is being treated as a string rather than as dates. To fix this, we’ll use the pandas
to_datetime()
feature which converts the arguments to dates.Lastly, we want to make sure that the Date column is the index column.
Now that our data has been converted into the desired format, let’s take a look at its columns for further analysis.
- The Open and Close columns indicate the opening and closing price of the stocks on a particular day.
- The High and Low columns provide the highest and the lowest price for the stock on a particular day, respectively.
- The Volume column tells us the total volume of stocks traded on a particular day.
The
Adj_Close
column represents the adjusted closing price, or the stock’s closing price on any given day of trading, amended to include any distributions and/or corporate actions occurring any time before the next day’s open. The adjusted closing price is often used when examining or performing a detailed analysis of historical returns.Interestingly, it appears that Amazon had a more or less steady increase in its stock price over the 2013-2018 window. We’ll now use pandas to analyze and manipulate this data to gain insights.
Pandas for time series analysis
As pandas was developed in the context of financial modeling, it contains a comprehensive set of tools for working with dates, times, and time-indexed data. Let’s look at the main pandas data structures for working with time series data.
Manipulating datetime
Python’s basic tools for working with dates and times reside in the built-in
datetime
module. In pandas, a single point in time is represented as a pandas.Timestamp
and we can use the datetime()
function to create datetime
objects from strings in a wide variety of date/time formats. datetimes are interchangeable with pandas.Timestamp
.We can now create a
datetime
object, and use it freely with pandas given the above attributes.For the purposes of analyzing our particular data, we have selected only the day, month and year, but we could also include more details like hour, minute and second if necessary.
For our stock price dataset, the type of the index column is
DatetimeIndex
. We can use pandas to obtain the minimum and maximum dates in the data.We can also calculate the latest date location and the earliest date index location as follows:
Time resampling
10 Minutes To Pandas Pdf
Examining stock price data for every single day isn’t of much use to financial institutions, who are more interested in spotting market trends. To make it easier, we use a process called time resampling to aggregate data into a defined time period, such as by month or by quarter. Institutions can then see an overview of stock prices and make decisions according to these trends.
The pandas library has a
resample()
function which resamples such time series data. The resample method in pandas is similar to its groupby
method as it is essentially grouping according to a certain time span. The resample()
function looks like this:To summarize:
data.resample()
is used to resample the stock data.- The ‘A’ stands for year-end frequency, and denotes the offset values by which we want to resample the data.
mean()
indicates that we want the average stock price during this period.
The output looks like this, with average stock data displayed for December 31st of each year
Below is a complete list of the offset values. The list can also be found in the pandas documentation.
We can also use time sampling to plot charts for specific columns.
The above bar plot corresponds to Amazon’s average adjusted closing price at year-end for each year in our data set.
Similarly, monthly maximum opening price for each year can be found below.
10 Minutes To Pandas Answer
Monthly maximum opening price for Amazon
Time shifting
Sometimes, we may need to shift or move the data forward or backwards in time. This shifting is done along a time index by the desired number of time-frequency increments.
Here is the original dataset before any time shifts.
Forward Shifting
To shift our data forward, we will pass the desired number of periods (or increments) through the shift() function, which needs to be positive value in this case.
Here we will move our data forward by one period or index, which means that all values which earlier corresponded to row N will now belong to row N+1. Here is the output:
Forward shifting by one index
Backwards shifting
To shift our data backwards, the number of periods (or increments) must be negative.
Backward shifting by one index
The opening amount corresponding to 2018–03–27 is now 1530, whereas originally it was 1572.40.
Shifting based off time string code
We can also use the offset from the offset table for time shifting. For that, we will use the pandas
shift()
function. We only need to pass in the periods
and freq
parameters. The period
attribute defines the number of steps to be shifted, while the freq
parameters denote the size of those steps.Let’s say we want to shift the data three months forward:
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Rolling windows
Time series data can be noisy due to high fluctuations in the market. As a result, it becomes difficult to gauge a trend or pattern in the data. Here is a visualization of the Amazon’s adjusted close price over the years where we can see such noise:
As we’re looking at daily data, there’s quite a bit of noise present. It would be nice if we could average this out by a week, which is where a rolling mean comes in. A rolling mean, or moving average, is a transformation method which helps average out noise from data. It works by simply splitting and aggregating the data into windows according to function, such as
mean()
, median()
, count()
, etc. For this example, we’ll use a rolling mean for 7 days.Here’s is the output:
The first six values have all become blank as there wasn’t enough data to actually fill them when using a window of seven days.
So, what are the key benefits of calculating a moving average or using this rolling mean method? Our data becomes a lot less noisy and more reflective of the trend than the data itself. Let’s actually plot this out. First, we’ll plot the original data followed by the rolling data for 30 days.
The orangeline is the originalopen price data. The blue line represents the 30-day rolling window, and has less noise than the orange line. Something to keep in mind is that once we run this code, the first 29 days aren’t going to have the blue line because there wasn’t enough data to actually calculate that rolling mean.
Conclusion
Python’s pandas library is a powerful, comprehensive library with a wide variety of inbuilt functions for analyzing time series data. In this article, we saw how pandas can be used for wrangling and visualizing time series data.
We also performed tasks like time sampling, time shifting and rolling with stock data. These are usually the first steps in analyzing any time series data. Going forward, we could use this data to perform a basic financial analysis by calculating the daily percentage change in stocks to get an idea about the volatility of stock prices. Another way we could use this data would beto predict Amazon’s stock prices for the next few days by employing machine learning techniques. This would be especially helpful from the shareholder’s point of view.
Example code for this article may be found at the Kite Blog repository on Github.
Here are links to the resources referenced in this article:
*You can view the original diagram with its context, here
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