Use the Pandas Dataframe Dept stats Again In Python
Scout At present This tutorial has a related video class created past the Real Python squad. Watch it together with the written tutorial to deepen your understanding: Explore Your Dataset With Pandas
Do you accept a big dataset that'southward full of interesting insights, but you're not sure where to outset exploring it? Has your boss asked you to generate some statistics from it, but they're not so easy to extract? These are precisely the use cases where Pandas and Python can help you! With these tools, yous'll be able to slice a large dataset downwardly into manageable parts and glean insight from that information.
In this tutorial, you lot'll acquire how to:
- Calculate metrics near your information
- Perform bones queries and aggregations
- Observe and handle incorrect data, inconsistencies, and missing values
- Visualize your data with plots
You'll also larn near the differences between the main information structures that Pandas and Python utilize. To follow forth, you tin can go all of the example lawmaking in this tutorial at the link below:
Setting Up Your Environment
In that location are a few things you lot'll need to become started with this tutorial. Start is a familiarity with Python's congenital-in data structures, especially lists and dictionaries. For more than information, check out Lists and Tuples in Python and Dictionaries in Python.
The 2d affair y'all'll need is a working Python environment. You tin can follow along in any terminal that has Python 3 installed. If you want to run into nicer output, especially for the large NBA dataset you'll exist working with, then you lot might want to run the examples in a Jupyter notebook.
The last matter you lot'll demand is Pandas and other Python libraries, which y'all can install with pip:
$ python3 -k pip install requests pandas matplotlib Y'all tin besides use the Conda parcel manager:
$ conda install requests pandas matplotlib If you're using the Anaconda distribution, then you're proficient to go! Anaconda already comes with the Pandas Python library installed.
The examples in this tutorial take been tested with Python 3.7 and Pandas 0.25.0, but they should also work in older versions. You can get all the code examples you'll see in this tutorial in a Jupyter notebook by clicking the link below:
Let's become started!
Using the Pandas Python Library
Now that you lot've installed Pandas, information technology'south time to have a look at a dataset. In this tutorial, y'all'll clarify NBA results provided past FiveThirtyEight in a 17MB CSV file. Create a script download_nba_all_elo.py to download the data:
import requests download_url = "https://raw.githubusercontent.com/fivethirtyeight/data/chief/nba-elo/nbaallelo.csv" target_csv_path = "nba_all_elo.csv" response = requests . get ( download_url ) response . raise_for_status () # Check that the request was successful with open ( target_csv_path , "wb" ) as f : f . write ( response . content ) print ( "Download fix." ) When you execute the script, it will salve the file nba_all_elo.csv in your electric current working directory.
Now yous tin can use the Pandas Python library to have a look at your data:
>>>
>>> import pandas as pd >>> nba = pd . read_csv ( "nba_all_elo.csv" ) >>> type ( nba ) <grade 'pandas.core.frame.DataFrame'> Here, you follow the convention of importing Pandas in Python with the pd alias. Then, you employ .read_csv() to read in your dataset and store information technology as a DataFrame object in the variable nba.
You can encounter how much information nba contains:
>>>
>>> len ( nba ) 126314 >>> nba . shape (126314, 23) You use the Python built-in role len() to decide the number of rows. You also utilize the .shape attribute of the DataFrame to run into its dimensionality. The result is a tuple containing the number of rows and columns.
Now you know that in that location are 126,314 rows and 23 columns in your dataset. But how tin you lot be certain the dataset really contains basketball game stats? You tin can have a look at the first five rows with .head():
If y'all're following along with a Jupyter notebook, and so you'll see a effect like this:
Unless your screen is quite large, your output probably won't display all 23 columns. Somewhere in the middle, you'll see a column of ellipses (...) indicating the missing information. If you lot're working in a terminal, and so that'due south probably more readable than wrapping long rows. However, Jupyter notebooks will allow you to scroll. You can configure Pandas to brandish all 23 columns similar this:
>>>
>>> pd . set_option ( "display.max.columns" , None ) While it'southward practical to see all the columns, you probably won't demand six decimal places! Modify it to two:
>>>
>>> pd . set_option ( "brandish.precision" , two ) To verify that you've changed the options successfully, you can execute .head() again, or you can display the last five rows with .tail() instead:
Now, you should see all the columns, and your data should prove ii decimal places:
You can discover some further possibilities of .caput() and .tail() with a small exercise. Tin you lot print the terminal three lines of your DataFrame? Expand the code block below to meet the solution:
Here's how to print the last three lines of nba:
Your output should look something like this:
You tin see the final 3 lines of your dataset with the options you've set up higher up.
Similar to the Python standard library, functions in Pandas also come with several optional parameters. Whenever you bump into an example that looks relevant just is slightly different from your use case, bank check out the official documentation. The chances are good that yous'll detect a solution by tweaking some optional parameters!
Getting to Know Your Information
You've imported a CSV file with the Pandas Python library and had a start look at the contents of your dataset. Then far, y'all've only seen the size of your dataset and its showtime and final few rows. Next, yous'll learn how to examine your data more than systematically.
Displaying Data Types
The starting time step in getting to know your data is to notice the different data types it contains. While you tin put anything into a list, the columns of a DataFrame comprise values of a specific information type. When yous compare Pandas and Python data structures, you'll see that this behavior makes Pandas much faster!
You can display all columns and their data types with .info():
This volition produce the following output:
You'll run across a listing of all the columns in your dataset and the blazon of data each column contains. Here, you tin can see the data types int64, float64, and object. Pandas uses the NumPy library to work with these types. Afterward, yous'll see the more complex categorical data type, which the Pandas Python library implements itself.
The object information type is a special one. According to the Pandas Cookbook, the object information type is "a catch-all for columns that Pandas doesn't recognize as any other specific type." In practice, it often means that all of the values in the column are strings.
Although you can store arbitrary Python objects in the object data type, you should exist enlightened of the drawbacks to doing so. Strange values in an object cavalcade can harm Pandas' functioning and its interoperability with other libraries. For more information, check out the official getting started guide.
Showing Nuts Statistics
Now that you lot've seen what information types are in your dataset, it's time to go an overview of the values each cavalcade contains. Yous can do this with .depict():
This part shows you some basic descriptive statistics for all numeric columns:
.draw() only analyzes numeric columns by default, but you can provide other data types if you utilize the include parameter:
>>>
>>> import numpy equally np >>> nba . describe ( include = object ) .describe() won't try to summate a hateful or a standard deviation for the object columns, since they more often than not include text strings. Still, it volition even so display some descriptive statistics:
Take a wait at the team_id and fran_id columns. Your dataset contains 104 different team IDs, but just 53 dissimilar franchise IDs. Furthermore, the nearly frequent team ID is BOS, just the nearly frequent franchise ID Lakers. How is that possible? Yous'll need to explore your dataset a bit more to answer this question.
Exploring Your Dataset
Exploratory data assay tin can assist you reply questions nearly your dataset. For instance, you can examine how frequently specific values occur in a cavalcade:
>>>
>>> nba [ "team_id" ] . value_counts () BOS 5997 NYK 5769 LAL 5078 ... SDS 11 >>> nba [ "fran_id" ] . value_counts () Proper noun: team_id, Length: 104, dtype: int64 Lakers 6024 Celtics 5997 Knicks 5769 ... Huskies lx Proper noun: fran_id, dtype: int64 It seems that a team named "Lakers" played 6024 games, merely only 5078 of those were played by the Los Angeles Lakers. Find out who the other "Lakers" team is:
>>>
>>> nba . loc [ nba [ "fran_id" ] == "Lakers" , "team_id" ] . value_counts () LAL 5078 MNL 946 Name: team_id, dtype: int64 Indeed, the Minneapolis Lakers ("MNL") played 946 games. Yous can even find out when they played those games. For that, you'll first define a column that converts the value of date_game to the datetime data type. Then you can apply the min and max aggregate functions, to find the get-go and terminal games of Minneapolis Lakers:
>>>
>>> nba [ "date_played" ] = pd . to_datetime ( nba [ "date_game" ]) >>> nba . loc [ nba [ "team_id" ] == "MNL" , "date_played" ] . min () Timestamp('1948-xi-04 00:00:00') >>> nba . loc [ nba [ 'team_id' ] == 'MNL' , 'date_played' ] . max () Timestamp('1960-03-26 00:00:00') >>> nba . loc [ nba [ "team_id" ] == "MNL" , "date_played" ] . agg (( "min" , "max" )) min 1948-11-04 max 1960-03-26 Name: date_played, dtype: datetime64[ns] It looks similar the Minneapolis Lakers played between the years of 1948 and 1960. That explains why yous might not recognize this squad!
You've also found out why the Boston Celtics team "BOS" played the most games in the dataset. Let's analyze their history as well a little bit. Find out how many points the Boston Celtics take scored during all matches contained in this dataset. Expand the code block below for the solution:
Similar to the .min() and .max() amass functions, you can likewise utilise .sum():
>>>
>>> nba . loc [ nba [ "team_id" ] == "BOS" , "pts" ] . sum () 626484 The Boston Celtics scored a full of 626,484 points.
You've got a taste for the capabilities of a Pandas DataFrame. In the following sections, y'all'll expand on the techniques you've but used, merely commencement, you'll zoom in and learn how this powerful data construction works.
Getting to Know Pandas' Data Structures
While a DataFrame provides functions that can feel quite intuitive, the underlying concepts are a bit trickier to understand. For this reason, y'all'll ready aside the vast NBA DataFrame and build some smaller Pandas objects from scratch.
Understanding Series Objects
Python'southward most basic data structure is the listing, which is also a skilful starting point for getting to know pandas.Series objects. Create a new Series object based on a list:
>>>
>>> revenues = pd . Series ([ 5555 , 7000 , 1980 ]) >>> revenues 0 5555 1 7000 2 1980 dtype: int64 Yous've used the list [5555, 7000, 1980] to create a Serial object called revenues. A Series object wraps two components:
- A sequence of values
- A sequence of identifiers, which is the index
Yous can access these components with .values and .index, respectively:
>>>
>>> revenues . values array([5555, 7000, 1980]) >>> revenues . index RangeIndex(offset=0, cease=iii, step=1) revenues.values returns the values in the Series, whereas revenues.alphabetize returns the positional index.
While Pandas builds on NumPy, a significant difference is in their indexing. Simply like a NumPy array, a Pandas Series also has an integer index that'south implicitly defined. This implicit index indicates the element'south position in the Series.
Nonetheless, a Serial can also take an arbitrary type of alphabetize. Yous can remember of this explicit index as labels for a specific row:
>>>
>>> city_revenues = pd . Serial ( ... [ 4200 , 8000 , 6500 ], ... index = [ "Amsterdam" , "Toronto" , "Tokyo" ] ... ) >>> city_revenues Amsterdam 4200 Toronto 8000 Tokyo 6500 dtype: int64 Here, the index is a list of city names represented by strings. Yous may have noticed that Python dictionaries employ cord indices likewise, and this is a handy analogy to keep in mind! Yous can apply the lawmaking blocks in a higher place to distinguish between two types of Series:
-
revenues: ThisSerialbehaves like a Python listing considering it only has a positional index. -
city_revenues: ThisSeriesacts like a Python lexicon because information technology features both a positional and a label index.
Here's how to construct a Serial with a label alphabetize from a Python dictionary:
>>>
>>> city_employee_count = pd . Series ({ "Amsterdam" : five , "Tokyo" : 8 }) >>> city_employee_count Amsterdam 5 Tokyo eight dtype: int64 The dictionary keys become the index, and the dictionary values are the Series values.
Simply like dictionaries, Series besides support .keys() and the in keyword:
>>>
>>> city_employee_count . keys () Index(['Amsterdam', 'Tokyo'], dtype='object') >>> "Tokyo" in city_employee_count True >>> "New York" in city_employee_count Faux You can use these methods to answer questions nearly your dataset quickly.
Agreement DataFrame Objects
While a Series is a pretty powerful information construction, it has its limitations. For example, you can only store one attribute per key. As y'all've seen with the nba dataset, which features 23 columns, the Pandas Python library has more than to offering with its DataFrame . This data structure is a sequence of Series objects that share the same index.
If you lot've followed along with the Series examples, then you should already have two Series objects with cities as keys:
-
city_revenues -
city_employee_count
You can combine these objects into a DataFrame past providing a lexicon in the constructor. The dictionary keys volition become the cavalcade names, and the values should contain the Series objects:
>>>
>>> city_data = pd . DataFrame ({ ... "acquirement" : city_revenues , ... "employee_count" : city_employee_count ... }) >>> city_data acquirement employee_count Amsterdam 4200 v.0 Tokyo 6500 eight.0 Toronto 8000 NaN Note how Pandas replaced the missing employee_count value for Toronto with NaN.
The new DataFrame index is the union of the two Series indices:
>>>
>>> city_data . index Index(['Amsterdam', 'Tokyo', 'Toronto'], dtype='object') Just like a Series, a DataFrame as well stores its values in a NumPy array:
>>>
>>> city_data . values array([[iv.2e+03, 5.0e+00], [6.5e+03, 8.0e+00], [8.0e+03, nan]]) You tin can besides refer to the ii dimensions of a DataFrame as axes:
>>>
>>> city_data . axes [Alphabetize(['Amsterdam', 'Tokyo', 'Toronto'], dtype='object'), Index(['revenue', 'employee_count'], dtype='object')] >>> city_data . axes [ 0 ] Index(['Amsterdam', 'Tokyo', 'Toronto'], dtype='object') >>> city_data . axes [ ane ] Index(['revenue', 'employee_count'], dtype='object') The axis marked with 0 is the row index, and the centrality marked with ane is the column alphabetize. This terminology is of import to know because you'll come across several DataFrame methods that accept an axis parameter.
A DataFrame is also a dictionary-similar data structure, so it besides supports .keys() and the in keyword. Even so, for a DataFrame these don't chronicle to the index, but to the columns:
>>>
>>> city_data . keys () Index(['revenue', 'employee_count'], dtype='object') >>> "Amsterdam" in city_data Fake >>> "acquirement" in city_data True You can come across these concepts in action with the bigger NBA dataset. Does it comprise a cavalcade called "points", or was information technology called "pts"? To answer this question, display the index and the axes of the nba dataset, then expand the code block below for the solution:
Considering y'all didn't specify an alphabetize column when you read in the CSV file, Pandas has assigned a RangeIndex to the DataFrame:
>>>
>>> nba . index RangeIndex(start=0, finish=126314, stride=ane) nba, similar all DataFrame objects, has two axes:
>>>
>>> nba . axes [RangeIndex(start=0, stop=126314, step=1), Index(['gameorder', 'game_id', 'lg_id', '_iscopy', 'year_id', 'date_game', 'seasongame', 'is_playoffs', 'team_id', 'fran_id', 'pts', 'elo_i', 'elo_n', 'win_equiv', 'opp_id', 'opp_fran', 'opp_pts', 'opp_elo_i', 'opp_elo_n', 'game_location', 'game_result', 'forecast', 'notes'], dtype='object')] You can check the existence of a column with .keys():
>>>
>>> "points" in nba . keys () False >>> "pts" in nba . keys () True The column is called "pts", not "points".
As you use these methods to answer questions virtually your dataset, exist certain to continue in listen whether y'all're working with a Serial or a DataFrame so that your interpretation is accurate.
Accessing Series Elements
In the section above, yous've created a Pandas Series based on a Python listing and compared the two data structures. You've seen how a Series object is like to lists and dictionaries in several ways. A further similarity is that you lot can employ the indexing operator ([]) for Serial likewise.
You'll also learn how to use two Pandas-specific admission methods:
-
.loc -
.iloc
You'll see that these data access methods tin can be much more readable than the indexing operator.
Using the Indexing Operator
Recall that a Series has two indices:
- A positional or implicit alphabetize, which is ever a
RangeIndex - A label or explicit alphabetize, which can comprise whatsoever hashable objects
Next, revisit the city_revenues object:
>>>
>>> city_revenues Amsterdam 4200 Toronto 8000 Tokyo 6500 dtype: int64 You lot can conveniently access the values in a Serial with both the label and positional indices:
>>>
>>> city_revenues [ "Toronto" ] 8000 >>> city_revenues [ 1 ] 8000 You lot can likewise utilise negative indices and slices, only like you would for a list:
>>>
>>> city_revenues [ - ane ] 6500 >>> city_revenues [ one :] Toronto 8000 Tokyo 6500 dtype: int64 >>> city_revenues [ "Toronto" :] Toronto 8000 Tokyo 6500 dtype: int64 If yous desire to larn more about the possibilities of the indexing operator, then bank check out Lists and Tuples in Python.
Using .loc and .iloc
The indexing operator ([]) is convenient, only at that place's a caveat. What if the labels are too numbers? Say you lot have to piece of work with a Series object similar this:
>>>
>>> colors = pd . Series ( ... [ "red" , "majestic" , "blue" , "greenish" , "yellow" ], ... index = [ 1 , 2 , 3 , 5 , eight ] ... ) >>> colors 1 blood-red ii purple 3 blue v green 8 yellow dtype: object What will colors[1] return? For a positional index, colors[1] is "purple". Yet, if you go by the characterization index, and then colors[1] is referring to "red".
The expert news is, you don't have to effigy it out! Instead, to avoid confusion, the Pandas Python library provides two information access methods:
-
.locrefers to the characterization index. -
.ilocrefers to the positional index.
These data access methods are much more readable:
>>>
>>> colors . loc [ 1 ] 'red' >>> colors . iloc [ 1 ] 'purple' colors.loc[1] returned "scarlet", the element with the label 1. colors.iloc[1] returned "purple", the element with the alphabetize 1.
The following figure shows which elements .loc and .iloc refer to:
Again, .loc points to the characterization index on the right-hand side of the image. Meanwhile, .iloc points to the positional index on the left-hand side of the motion picture.
It'southward easier to keep in mind the distinction betwixt .loc and .iloc than it is to effigy out what the indexing operator will return. Fifty-fifty if yous're familiar with all the quirks of the indexing operator, it can be dangerous to assume that everybody who reads your code has internalized those rules besides!
.loc and .iloc also support the features yous would expect from indexing operators, like slicing. Yet, these data access methods have an of import difference. While .iloc excludes the closing element, .loc includes information technology. Take a look at this code cake:
>>>
>>> # Return the elements with the implicit index: 1, 2 >>> colors . iloc [ 1 : 3 ] two purple iii bluish dtype: object If you compare this code with the image above, so you tin come across that colors.iloc[1:3] returns the elements with the positional indices of 1 and 2. The closing particular "green" with a positional index of 3 is excluded.
On the other mitt, .loc includes the closing element:
>>>
>>> # Return the elements with the explicit alphabetize betwixt 3 and 8 >>> colors . loc [ iii : viii ] 3 blue 5 green viii yellow dtype: object This code block says to return all elements with a characterization index between 3 and 8. Hither, the endmost detail "yellow" has a characterization alphabetize of 8 and is included in the output.
You can also pass a negative positional index to .iloc:
>>>
>>> colors . iloc [ - two ] 'green' You beginning from the cease of the Series and return the second element.
You tin use the code blocks above to distinguish betwixt two Series behaviors:
- You tin can use
.ilocon aSeriessimilar to using[]on a listing. - You can utilize
.locon aSeriessimilar to using[]on a dictionary.
Be sure to keep these distinctions in mind as you admission elements of your Series objects.
Accessing DataFrame Elements
Since a DataFrame consists of Serial objects, you can use the very same tools to access its elements. The crucial divergence is the additional dimension of the DataFrame. You'll use the indexing operator for the columns and the access methods .loc and .iloc on the rows.
Using the Indexing Operator
If you think of a DataFrame as a lexicon whose values are Series, so it makes sense that you can access its columns with the indexing operator:
>>>
>>> city_data [ "acquirement" ] Amsterdam 4200 Tokyo 6500 Toronto 8000 Name: revenue, dtype: int64 >>> type ( city_data [ "revenue" ]) pandas.core.series.Serial Hither, you use the indexing operator to select the column labeled "revenue".
If the cavalcade name is a string, then y'all can use aspect-style accessing with dot note as well:
>>>
>>> city_data . revenue Amsterdam 4200 Tokyo 6500 Toronto 8000 Name: revenue, dtype: int64 city_data["revenue"] and city_data.revenue return the same output.
There's i situation where accessing DataFrame elements with dot annotation may not work or may lead to surprises. This is when a cavalcade proper name coincides with a DataFrame aspect or method name:
>>>
>>> toys = pd . DataFrame ([ ... { "name" : "ball" , "shape" : "sphere" }, ... { "proper noun" : "Rubik'due south cube" , "shape" : "cube" } ... ]) >>> toys [ "shape" ] 0 sphere ane cube Name: shape, dtype: object >>> toys . shape (two, 2) The indexing operation toys["shape"] returns the right data, merely the attribute-style functioning toys.shape still returns the shape of the DataFrame. You should merely utilise attribute-style accessing in interactive sessions or for read operations. You lot shouldn't use it for product code or for manipulating data (such as defining new columns).
Using .loc and .iloc
Like to Series, a DataFrame also provides .loc and .iloc information access methods. Call up, .loc uses the label and .iloc the positional index:
>>>
>>> city_data . loc [ "Amsterdam" ] acquirement 4200.0 employee_count 5.0 Name: Amsterdam, dtype: float64 >>> city_data . loc [ "Tokyo" : "Toronto" ] revenue employee_count Tokyo 6500 8.0 Toronto 8000 NaN >>> city_data . iloc [ i ] revenue 6500.0 employee_count 8.0 Proper noun: Tokyo, dtype: float64 Each line of code selects a dissimilar row from city_data:
-
city_data.loc["Amsterdam"]selects the row with the label alphabetize"Amsterdam". -
city_data.loc["Tokyo": "Toronto"]selects the rows with characterization indices from"Tokyo"to"Toronto". Remember,.locis inclusive. -
city_data.iloc[one]selects the row with the positional alphabetize1, which is"Tokyo".
Alright, you've used .loc and .iloc on small data structures. At present, it's time to practice with something bigger! Use a data access method to display the 2d-to-terminal row of the nba dataset. Then, expand the code block below to run across a solution:
The second-to-final row is the row with the positional index of -2. You can display it with .iloc:
>>>
>>> nba . iloc [ - 2 ] gameorder 63157 game_id 201506170CLE lg_id NBA _iscopy 0 year_id 2015 date_game 6/16/2015 seasongame 102 is_playoffs 1 team_id CLE fran_id Cavaliers pts 97 elo_i 1700.74 elo_n 1692.09 win_equiv 59.29 opp_id GSW opp_fran Warriors opp_pts 105 opp_elo_i 1813.63 opp_elo_n 1822.29 game_location H game_result Fifty forecast 0.48 notes NaN date_played 2015-06-16 00:00:00 Name: 126312, dtype: object You'll come across the output as a Series object.
For a DataFrame, the information admission methods .loc and .iloc also take a 2nd parameter. While the starting time parameter selects rows based on the indices, the second parameter selects the columns. You lot can use these parameters together to select a subset of rows and columns from your DataFrame:
>>>
>>> city_data . loc [ "Amsterdam" : "Tokyo" , "revenue" ] Amsterdam 4200 Tokyo 6500 Proper noun: revenue, dtype: int64 Annotation that you split up the parameters with a comma (,). The commencement parameter, "Amsterdam" : "Tokyo," says to select all rows between those two labels. The second parameter comes after the comma and says to select the "acquirement" column.
It's time to encounter the same construct in action with the bigger nba dataset. Select all games between the labels 5555 and 5559. You lot're only interested in the names of the teams and the scores, so select those elements as well. Expand the lawmaking cake below to see a solution:
Outset, define which rows you lot want to see, and then listing the relevant columns:
>>>
>>> nba . loc [ 5555 : 5559 , [ "fran_id" , "opp_fran" , "pts" , "opp_pts" ]] Yous use .loc for the characterization index and a comma (,) to carve up your ii parameters.
You should see a small part of your quite huge dataset:
The output is much easier to read!
With data access methods like .loc and .iloc, you tin can select only the right subset of your DataFrame to help you answer questions nearly your dataset.
Querying Your Dataset
You've seen how to access subsets of a huge dataset based on its indices. At present, you lot'll select rows based on the values in your dataset's columns to query your data. For example, you can create a new DataFrame that contains only games played later 2010:
>>>
>>> current_decade = nba [ nba [ "year_id" ] > 2010 ] >>> current_decade . shape (12658, 24) Yous now have 24 columns, but your new DataFrame only consists of rows where the value in the "year_id" column is greater than 2010.
Y'all can as well select the rows where a specific field is not null:
>>>
>>> games_with_notes = nba [ nba [ "notes" ] . notnull ()] >>> games_with_notes . shape (5424, 24) This tin be helpful if you desire to avoid any missing values in a column. You tin can also employ .notna() to attain the same goal.
You can even access values of the object information type as str and perform cord methods on them:
>>>
>>> ers = nba [ nba [ "fran_id" ] . str . endswith ( "ers" )] >>> ers . shape (27797, 24) You employ .str.endswith() to filter your dataset and find all games where the habitation team's name ends with "ers".
Y'all tin combine multiple criteria and query your dataset as well. To do this, exist sure to put each one in parentheses and apply the logical operators | and & to separate them.
Do a search for Baltimore games where both teams scored over 100 points. In order to see each game only one time, yous'll need to exclude duplicates:
>>>
>>> nba [ ... ( nba [ "_iscopy" ] == 0 ) & ... ( nba [ "pts" ] > 100 ) & ... ( nba [ "opp_pts" ] > 100 ) & ... ( nba [ "team_id" ] == "BLB" ) ... ] Here, you use nba["_iscopy"] == 0 to include only the entries that aren't copies.
Your output should comprise five eventful games:
Try to build some other query with multiple criteria. In the spring of 1992, both teams from Los Angeles had to play a home game at another courtroom. Query your dataset to find those two games. Both teams take an ID starting with "LA". Expand the code block below to see a solution:
You can utilize .str to find the squad IDs that start with "LA", and you tin can presume that such an unusual game would accept some notes:
>>>
>>> nba [ ... ( nba [ "_iscopy" ] == 0 ) & ... ( nba [ "team_id" ] . str . startswith ( "LA" )) & ... ( nba [ "year_id" ] == 1992 ) & ... ( nba [ "notes" ] . notnull ()) ... ] Your output should evidence 2 games on the mean solar day v/3/1992:
Overnice find!
When you know how to query your dataset with multiple criteria, you'll exist able to answer more than specific questions virtually your dataset.
Grouping and Accumulation Your Data
Y'all may also want to learn other features of your dataset, like the sum, mean, or average value of a group of elements. Luckily, the Pandas Python library offers grouping and aggregation functions to assist you accomplish this task.
A Series has more than than twenty dissimilar methods for calculating descriptive statistics. Here are some examples:
>>>
>>> city_revenues . sum () 18700 >>> city_revenues . max () 8000 The first method returns the total of city_revenues, while the second returns the max value. There are other methods yous can use, like .min() and .hateful().
Remember, a column of a DataFrame is actually a Series object. For this reason, you lot can employ these aforementioned functions on the columns of nba:
>>>
>>> points = nba [ "pts" ] >>> blazon ( points ) <class 'pandas.core.series.Series'> >>> points . sum () 12976235 A DataFrame can have multiple columns, which introduces new possibilities for aggregations, like grouping:
>>>
>>> nba . groupby ( "fran_id" , sort = False )[ "pts" ] . sum () fran_id Huskies 3995 Knicks 582497 Stags 20398 Falcons 3797 Capitols 22387 ... By default, Pandas sorts the grouping keys during the telephone call to .groupby(). If you don't want to sort, then pass sort=False. This parameter can lead to performance gains.
You can also grouping by multiple columns:
>>>
>>> nba [ ... ( nba [ "fran_id" ] == "Spurs" ) & ... ( nba [ "year_id" ] > 2010 ) ... ] . groupby ([ "year_id" , "game_result" ])[ "game_id" ] . count () year_id game_result 2011 L 25 Due west 63 2012 Fifty 20 Westward 60 2013 L 30 W 73 2014 50 27 W 78 2015 L 31 W 58 Name: game_id, dtype: int64 Y'all tin exercise these basics with an exercise. Take a wait at the Golden Land Warriors' 2014-fifteen season (year_id: 2015). How many wins and losses did they score during the regular flavour and the playoffs? Expand the code block below for the solution:
First, you can group by the "is_playoffs" field, then by the outcome:
>>>
>>> nba [ ... ( nba [ "fran_id" ] == "Warriors" ) & ... ( nba [ "year_id" ] == 2015 ) ... ] . groupby ([ "is_playoffs" , "game_result" ])[ "game_id" ] . count () is_playoffs game_result 0 Fifty fifteen Due west 67 1 L 5 W sixteen is_playoffs=0 shows the results for the regular season, and is_playoffs=1 shows the results for the playoffs.
In the examples to a higher place, you've just scratched the surface of the aggregation functions that are bachelor to y'all in the Pandas Python library. To see more examples of how to use them, check out Pandas GroupBy: Your Guide to Group Data in Python.
Manipulating Columns
You'll need to know how to manipulate your dataset's columns in different phases of the information analysis process. You can add and drop columns as role of the initial data cleaning phase, or later on based on the insights of your analysis.
Create a copy of your original DataFrame to work with:
>>>
>>> df = nba . copy () >>> df . shape (126314, 24) You can define new columns based on the existing ones:
>>>
>>> df [ "deviation" ] = df . pts - df . opp_pts >>> df . shape (126314, 25) Here, yous used the "pts" and "opp_pts" columns to create a new one chosen "difference". This new column has the aforementioned functions every bit the old ones:
>>>
>>> df [ "difference" ] . max () 68 Hither, you used an assemblage office .max() to find the largest value of your new cavalcade.
You can likewise rename the columns of your dataset. It seems that "game_result" and "game_location" are too verbose, and then go ahead and rename them at present:
>>>
>>> renamed_df = df . rename ( ... columns = { "game_result" : "result" , "game_location" : "location" } ... ) >>> renamed_df . info () <grade 'pandas.cadre.frame.DataFrame'> RangeIndex: 126314 entries, 0 to 126313 Data columns (total 25 columns): # Column Non-Cypher Count Dtype --- ------ -------------- ----- 0 gameorder 126314 non-null int64 ... xix location 126314 non-null object xx result 126314 non-zippo object 21 forecast 126314 non-null float64 22 notes 5424 not-null object 23 date_played 126314 non-null datetime64[ns] 24 divergence 126314 not-null int64 dtypes: datetime64[ns](ane), float64(6), int64(8), object(10) retentiveness usage: 24.ane+ MB Note that there's a new object, renamed_df. Like several other data manipulation methods, .rename() returns a new DataFrame by default. If you want to manipulate the original DataFrame directly, then .rename() also provides an inplace parameter that you can gear up to Truthful.
Your dataset might comprise columns that you don't need. For case, Elo ratings may be a fascinating concept to some, but you won't clarify them in this tutorial. You lot can delete the iv columns related to Elo:
>>>
>>> df . shape (126314, 25) >>> elo_columns = [ "elo_i" , "elo_n" , "opp_elo_i" , "opp_elo_n" ] >>> df . drib ( elo_columns , inplace = True , axis = ane ) >>> df . shape (126314, 21) Remember, yous added the new column "difference" in a previous case, bringing the total number of columns to 25. When you remove the four Elo columns, the total number of columns drops to 21.
Specifying Data Types
When you create a new DataFrame, either by calling a constructor or reading a CSV file, Pandas assigns a data type to each cavalcade based on its values. While it does a pretty skillful job, it's not perfect. If you choose the correct data type for your columns upfront, then you tin can significantly improve your code's performance.
Accept another look at the columns of the nba dataset:
Yous'll run into the same output as before:
Ten of your columns have the data type object. Most of these object columns incorporate arbitrary text, but in that location are too some candidates for data type conversion. For example, take a wait at the date_game column:
>>>
>>> df [ "date_game" ] = pd . to_datetime ( df [ "date_game" ]) Here, you employ .to_datetime() to specify all game dates as datetime objects.
Other columns contain text that are a bit more structured. The game_location column tin can have simply three different values:
>>>
>>> df [ "game_location" ] . nunique () 3 >>> df [ "game_location" ] . value_counts () A 63138 H 63138 N 38 Name: game_location, dtype: int64 Which data type would you employ in a relational database for such a column? You would probably non use a varchar type, but rather an enum. Pandas provides the chiselled data blazon for the same purpose:
>>>
>>> df [ "game_location" ] = pd . Categorical ( df [ "game_location" ]) >>> df [ "game_location" ] . dtype CategoricalDtype(categories=['A', 'H', 'Northward'], ordered=False) chiselled data has a few advantages over unstructured text. When yous specify the chiselled data blazon, yous make validation easier and save a ton of memory, every bit Pandas will only use the unique values internally. The higher the ratio of full values to unique values, the more than infinite savings you'll get.
Run df.info() again. You should see that changing the game_location data type from object to categorical has decreased the memory usage.
You'll often encounter datasets with too many text columns. An essential skill for information scientists to take is the ability to spot which columns they can convert to a more performant data blazon.
Take a moment to practice this at present. Observe some other column in the nba dataset that has a generic data type and convert information technology to a more specific one. You can expand the code block below to see one potential solution:
game_result can take only two different values:
>>>
>>> df [ "game_result" ] . nunique () 2 >>> df [ "game_result" ] . value_counts () L 63157 W 63157 To improve operation, you tin catechumen it into a categorical column:
>>>
>>> df [ "game_result" ] = pd . Categorical ( df [ "game_result" ]) You can use df.info() to bank check the memory usage.
As yous work with more than massive datasets, memory savings becomes especially crucial. Be sure to keep performance in mind as you lot continue to explore your datasets.
Cleaning Data
You may be surprised to find this section so late in the tutorial! Unremarkably, you'd accept a critical expect at your dataset to fix any issues before you lot move on to a more sophisticated analysis. All the same, in this tutorial, you'll rely on the techniques that you've learned in the previous sections to clean your dataset.
Missing Values
Have you ever wondered why .info() shows how many non-zippo values a column contains? The reason why is that this is vital information. Nothing values often indicate a problem in the information-gathering procedure. They can brand several assay techniques, like different types of machine learning, difficult or fifty-fifty incommunicable.
When yous audit the nba dataset with nba.info(), you'll come across that it's quite neat. Simply the column notes contains nothing values for the majority of its rows:
This output shows that the notes column has only 5424 non-nothing values. That ways that over 120,000 rows of your dataset have null values in this cavalcade.
Sometimes, the easiest mode to deal with records containing missing values is to ignore them. You can remove all the rows with missing values using .dropna():
>>>
>>> rows_without_missing_data = nba . dropna () >>> rows_without_missing_data . shape (5424, 24) Of course, this kind of data cleanup doesn't make sense for your nba dataset, because it'south not a problem for a game to lack notes. Just if your dataset contains a million valid records and a hundred where relevant data is missing, then dropping the incomplete records can be a reasonable solution.
You tin as well driblet problematic columns if they're non relevant for your analysis. To practice this, use .dropna() over again and provide the centrality=1 parameter:
>>>
>>> data_without_missing_columns = nba . dropna ( axis = 1 ) >>> data_without_missing_columns . shape (126314, 23) Now, the resulting DataFrame contains all 126,314 games, merely non the sometimes empty notes cavalcade.
If there's a meaningful default value for your use example, then yous can likewise supervene upon the missing values with that:
>>>
>>> data_with_default_notes = nba . copy () >>> data_with_default_notes [ "notes" ] . fillna ( ... value = "no notes at all" , ... inplace = True ... ) >>> data_with_default_notes [ "notes" ] . describe () count 126314 unique 232 top no notes at all freq 120890 Name: notes, dtype: object Here, you fill the empty notes rows with the cord "no notes at all".
Invalid Values
Invalid values tin be even more dangerous than missing values. Often, y'all can perform your data analysis equally expected, but the results you get are peculiar. This is especially of import if your dataset is enormous or used manual entry. Invalid values are often more challenging to detect, just you can implement some sanity checks with queries and aggregations.
One thing yous can do is validate the ranges of your data. For this, .describe() is quite handy. Recall that information technology returns the following output:
The year_id varies betwixt 1947 and 2015. That sounds plausible.
What about pts? How can the minimum exist 0? Let'due south take a expect at those games:
>>>
>>> nba [ nba [ "pts" ] == 0 ] This query returns a single row:
It seems the game was forfeited. Depending on your analysis, you may want to remove it from the dataset.
Inconsistent Values
Sometimes a value would be entirely realistic in and of itself, but it doesn't fit with the values in the other columns. You can ascertain some query criteria that are mutually exclusive and verify that these don't occur together.
In the NBA dataset, the values of the fields pts, opp_pts and game_result should be consequent with each other. You can bank check this using the .empty attribute:
>>>
>>> nba [( nba [ "pts" ] > nba [ "opp_pts" ]) & ( nba [ "game_result" ] != 'W' )] . empty True >>> nba [( nba [ "pts" ] < nba [ "opp_pts" ]) & ( nba [ "game_result" ] != '50' )] . empty True Fortunately, both of these queries return an empty DataFrame.
Be prepared for surprises whenever you lot're working with raw datasets, peculiarly if they were gathered from different sources or through a circuitous pipeline. You lot might see rows where a team scored more points than their opponent, but yet didn't win—at to the lowest degree, co-ordinate to your dataset! To avoid situations like this, make certain you add further data cleaning techniques to your Pandas and Python arsenal.
Combining Multiple Datasets
In the previous section, yous've learned how to clean a messy dataset. Another attribute of real-world information is that it often comes in multiple pieces. In this department, you lot'll larn how to grab those pieces and combine them into one dataset that'southward ready for analysis.
Earlier, yous combined two Series objects into a DataFrame based on their indices. Now, yous'll have this 1 step farther and use .concat() to combine city_data with another DataFrame. Say you lot've managed to gather some information on two more cities:
>>>
>>> further_city_data = pd . DataFrame ( ... { "revenue" : [ 7000 , 3400 ], "employee_count" :[ two , 2 ]}, ... index = [ "New York" , "Barcelona" ] ... ) This second DataFrame contains info on the cities "New York" and "Barcelona".
You tin can add these cities to city_data using .concat():
>>>
>>> all_city_data = pd . concat ([ city_data , further_city_data ], sort = False ) >>> all_city_data Amsterdam 4200 5.0 Tokyo 6500 eight.0 Toronto 8000 NaN New York 7000 2.0 Barcelona 3400 2.0 Now, the new variable all_city_data contains the values from both DataFrame objects.
By default, concat() combines along axis=0. In other words, information technology appends rows. Yous can likewise employ it to append columns by supplying the parameter axis=1:
>>>
>>> city_countries = pd . DataFrame ({ ... "country" : [ "The netherlands" , "Japan" , "Holland" , "Canada" , "Spain" ], ... "uppercase" : [ 1 , one , 0 , 0 , 0 ]}, ... index = [ "Amsterdam" , "Tokyo" , "Rotterdam" , "Toronto" , "Barcelona" ] ... ) >>> cities = pd . concat ([ all_city_data , city_countries ], axis = 1 , sort = Faux ) >>> cities revenue employee_count land uppercase Amsterdam 4200.0 5.0 Kingdom of the netherlands 1.0 Tokyo 6500.0 8.0 Japan 1.0 Toronto 8000.0 NaN Canada 0.0 New York 7000.0 2.0 NaN NaN Barcelona 3400.0 2.0 Spain 0.0 Rotterdam NaN NaN Holland 0.0 Notation how Pandas added NaN for the missing values. If you want to combine but the cities that appear in both DataFrame objects, then you can ready the join parameter to inner:
>>>
>>> pd . concat ([ all_city_data , city_countries ], centrality = i , join = "inner" ) revenue employee_count country uppercase Amsterdam 4200 five.0 Holland 1 Tokyo 6500 8.0 Japan 1 Toronto 8000 NaN Canada 0 Barcelona 3400 2.0 Spain 0 While information technology'south nigh straightforward to combine data based on the alphabetize, it's not the only possibility. You tin apply .merge() to implement a bring together operation like to the one from SQL:
>>>
>>> countries = pd . DataFrame ({ ... "population_millions" : [ 17 , 127 , 37 ], ... "continent" : [ "Europe" , "Asia" , "North America" ] ... }, alphabetize = [ "Holland" , "Nihon" , "Canada" ]) >>> pd . merge ( cities , countries , left_on = "country" , right_index = True ) Hither, you pass the parameter left_on="country" to .merge() to betoken what cavalcade you desire to bring together on. The consequence is a bigger DataFrame that contains not only metropolis information, merely too the population and continent of the corresponding countries:
Note that the result contains only the cities where the country is known and appears in the joined DataFrame.
.merge() performs an inner join by default. If you want to include all cities in the effect, and so you need to provide the how parameter:
>>>
>>> pd . merge ( ... cities , ... countries , ... left_on = "state" , ... right_index = True , ... how = "left" ... ) With this left bring together, you'll see all the cities, including those without country data:
Welcome back, New York & Barcelona!
Visualizing Your Pandas DataFrame
Data visualization is ane of the things that works much ameliorate in a Jupyter notebook than in a last, so go ahead and fire one up. If you demand help getting started, then check out Jupyter Notebook: An Introduction. You can also access the Jupyter notebook that contains the examples from this tutorial by clicking the link below:
Include this line to show plots direct in the notebook:
>>>
>>> % matplotlib inline Both Serial and DataFrame objects accept a .plot() method, which is a wrapper effectually matplotlib.pyplot.plot(). By default, it creates a line plot. Visualize how many points the Knicks scored throughout the seasons:
>>>
>>> nba [ nba [ "fran_id" ] == "Knicks" ] . groupby ( "year_id" )[ "pts" ] . sum () . plot () This shows a line plot with several peaks and 2 notable valleys effectually the years 2000 and 2010:
Y'all can also create other types of plots, similar a bar plot:
>>>
>>> nba [ "fran_id" ] . value_counts () . head ( x ) . plot ( kind = "bar" ) This volition show the franchises with the most games played:
The Lakers are leading the Celtics by a minimal border, and in that location are six further teams with a game count in a higher place 5000.
Now attempt a more complicated exercise. In 2013, the Miami Heat won the championship. Create a pie plot showing the count of their wins and losses during that season. And so, expand the code block to see a solution:
Beginning, you define a criteria to include only the Heat's games from 2013. So, you create a plot in the same way as you've seen above:
>>>
>>> nba [ ... ( nba [ "fran_id" ] == "Heat" ) & ... ( nba [ "year_id" ] == 2013 ) ... ][ "game_result" ] . value_counts () . plot ( kind = "pie" ) Hither's what a champion pie looks like:
The slice of wins is significantly larger than the piece of losses!
Sometimes, the numbers speak for themselves, but frequently a chart helps a lot with communicating your insights. To learn more than about visualizing your data, check out Interactive Data Visualization in Python With Bokeh.
Conclusion
In this tutorial, you've learned how to start exploring a dataset with the Pandas Python library. You saw how yous could access specific rows and columns to tame even the largest of datasets. Speaking of taming, yous've also seen multiple techniques to prepare and clean your data, by specifying the data type of columns, dealing with missing values, and more. You've even created queries, aggregations, and plots based on those.
Now you can:
- Piece of work with
SerialandDataFrameobjects - Subset your information with
.loc,.iloc, and the indexing operator - Reply questions with queries, group, and aggregation
- Handle missing, invalid, and inconsistent information
- Visualize your dataset in a Jupyter notebook
This journeying using the NBA stats only scratches the surface of what y'all tin do with the Pandas Python library. You tin can power upwardly your project with Pandas tricks, larn techniques to speed upwardly Pandas in Python, and fifty-fifty dive deep to see how Pandas works behind the scenes. At that place are many more features for you lot to notice, so get out there and tackle those datasets!
You can go all the code examples you saw in this tutorial by clicking the link below:
Watch At present This tutorial has a related video grade created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Explore Your Dataset With Pandas
Source: https://realpython.com/pandas-python-explore-dataset/
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