Select rows from dataframe python

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Select rows from dataframe python

This introduction to pandas is derived from Data School's pandas Q&A with my own notes and code. Selecting multiple rows and columns from a pandas DataFrame? In [1]: In [3]: url = ' ufo = pd.read_csv(url) In [5]: # show first 3 shows ufo.head(3) Out[5]: .loc usage This is a really powerful and flexible method In [6]: # .loc DataFrame method # filtering rows and selecting columns by label # format # ufo.loc[rows, columns] # row 0, all columns ufo.loc[0, :] Out[6]: City Ithaca Colors Reported NaN Shape Reported TRIANGLE State NY Time 6/1/1930 22:00 Name: 0, dtype: object In [10]: # rows 0, 1, 2 # all columns ufo.loc[[0, 1, 2], :] # more efficient code ufo.loc[0:2, :] Out[10]: In [12]: # if you leave off ", :" pandas would assume it's there # but you should leave it there to improve code readability ufo.loc[0:2] Out[12]: In [13]: # all rows # column: City ufo.loc[:, 'City'] Out[13]: 0 Ithaca 1 Willingboro 2 Holyoke 3 Abilene 4 New York Worlds Fair 5 Valley City 6 Crater Lake 7 Alma 8 Eklutna 9 Hubbard 10 Fontana 11 Waterloo 12 Belton 13 Keokuk 14 Ludington 15 Forest Home 16 Los Angeles 17 Hapeville 18 Oneida 19 Bering Sea 20 Nebraska 21 NaN 22 NaN 23 Owensboro 24 Wilderness 25 San Diego 26 Wilderness 27 Clovis 28 Los Alamos 29 Ft. Duschene ... 18211 Holyoke 18212 Carson 18213 Pasadena 18214 Austin 18215 El Campo 18216 Garden Grove 18217 Berthoud Pass 18218 Sisterdale 18219 Garden Grove 18220 Shasta Lake 18221 Franklin 18222 Albrightsville 18223 Greenville 18224 Eufaula 18225 Simi Valley 18226 San Francisco 18227 San Francisco 18228 Kingsville 18229 Chicago 18230 Pismo Beach 18231 Pismo Beach 18232 Lodi 18233 Anchorage 18234 Capitola 18235 Fountain Hills 18236 Grant Park 18237 Spirit Lake 18238 Eagle River 18239 Eagle River 18240 Ybor Name: City, dtype: object In [15]: # all rows # column: City, State ufo.loc[:, ['City', 'State']] # similar code for City through State ufo.loc[:, 'City':'State'] Out[15]: In [17]: # multiple rows and multiple columns ufo.loc[0:2, 'City':'State'] Out[17]: In [18]: # filter using City=='Oakland' ufo[ufo.City=='Oakland'] Out[18]: In [20]: # easier-to-read code # here you specify the rows and columns you want # ufo.loc[rows, columns] ufo.loc[ufo.City=='Oakland', :] Out[20]: In [21]: # again, specifying the rows and columns you want # this would be the best way to do it compared to chain indexing ufo.loc[ufo.City=='Oakland', 'State'] Out[21]: 1694 CA 2144 CA 4686 MD 7293 CA 8488 CA 8768 CA 10816 OR 10948 CA 11045 CA 12322 CA 12941 CA 16803 MD 17322 CA Name: State, dtype: object In [24]: # chain indexing # there may be issues in some cases # try not to use this ufo[ufo.City=='Oakland'].State Out[24]: 1694 CA 2144 CA 4686 MD 7293 CA 8488 CA 8768 CA 10816 OR 10948 CA 11045 CA 12322 CA 12941 CA 16803 MD 17322 CA Name: State, dtype: object In [25]: Out[25]: In [28]: # iloc excludes 4 (compared to loc where it includes 4) # iloc includes 0 ufo.iloc[:, 0:4] Out[28]: In [31]: # this is the major difference # exclusive of 3 ufo.iloc[0:3, :] Out[31]: In [38]: # non-explicit code ufo[['City', 'State']] # explicit code ufo.loc[:, ['City', 'State']] Out[38]: In [40]: # ambiguous code again, are we referring to rows or columns? ufo[0:2] # use iloc! ufo.iloc[0:2, :] Out[40]: .ix usage Mix labels and integers when using selection. In [41]: drinks_url = ' drinks = pd.read_csv(drinks_url, index_col='country') Out[42]: In [43]: In [44]: drinks.ix[1, 'beer_servings'] In [46]: # for .ix, columns are exclusive of 2 drinks.ix['Albania':'Andorra', 0:2] Out[46]: In [48]: # for rows, .ix is inclusive from start to end # for columns, .ix is exclusive of end but inclusive of start ufo.ix[0:2, 0:2] Out[48]: Pandas DataFrame properties like iloc and loc are useful to select rows from DataFrame. There are multiple ways to select and index DataFrame rows. We can also select rows from pandas DataFrame based on the conditions specified. Logical selections and boolean Series can also be passed to the generic [] indexer of a pandas DataFrame and will give the same results.How to Select Rows from Pandas DataFramePandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object. Like Series, DataFrame accepts many different kinds of input:Steps to Select Rows from Pandas DataFrameStep 1: Data SetupPandas read_csv() is an inbuilt function that is used to import the data from a CSV file and analyze that data in Python. So, we will import the Dataset from the CSV file, and it will be automatically converted to Pandas DataFrame and then select the Data from DataFrame.The data set for our project is here: people.csvThe above Dataset has 18 rows and 5 columns.Step 2: Import CSV DataNow, put the file in our project folder and the same directory as our python programming file app.py.Write the following code inside the app.py file.# app.py import pandas as pd df = pd.read_csv('people.csv') print(df)Outputpython3 app.py Name Sex Age Height Weight 0 Alex M 41 74 170 1 Bert M 42 68 166 2 Carl M 32 70 155 3 Dave M 39 72 167 4 Elly F 30 66 124 5 Fran F 33 66 115 6 Gwen F 26 64 121 7 Hank M 30 71 158 8 Ivan M 53 72 175 9 Jake M 32 69 143 10 Kate F 47 69 139 11 Luke M 34 72 163 12 Myra F 23 62 98 13 Neil M 36 75 160 14 Omar M 38 70 145 15 Page F 31 67 135 16 Quin M 29 71 176 17 Ruth F 28 65 131So, our DataFrame is ready. The read_csv() function automatically converts CSV data into DataFrame when the import is complete.We can check the Data type using the Python type() function.# app.py import pandas as pd df = pd.read_csv('people.csv') print(type(df))Outputpython3 app.py Step 3: Select Rows from Pandas DataFrameSelect pandas rows using iloc propertyPandas iloc indexer for Pandas Dataframe is used for integer-location based indexing/selection by position. Pandas.DataFrame.iloc is a unique inbuilt method that returns integer-location based indexing for selection by position. The iloc indexer syntax is the following.df.iloc[, ]This is sure to be a source of confusion for R users. "iloc" in pandas is used to select rows and columns by number in the order that they appear in the DataFrame.That means if we pass df.iloc[6, 0], that means the 6th index row( row index starts from 0) and 0th column, which is the Name.So, the output will be according to our DataFrame is Gwen. Let's print this programmatically.See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') print(df.iloc[6, 0])Outputpython3 app.py GwenYou can imagine that each row has a row number from 0 to the total rows (data.shape[0]), and iloc[] allows selections based on these numbers. The same applies to all the columns (ranging from 0 to data.shape[1] ).In the above example, we have selected particular DataFrame value, but we can also select rows in DataFrame using iloc as well. See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') print(df.iloc[4])Outputpython3 app.py Name Elly Sex F Age 30 Height 66 Weight 124 Name: 4, dtype: objectSo, we have selected a single row using iloc[] property of DataFrame.If we pass the negative value to the iloc[] property that it will give us the last row of the DataFrame. # app.py import pandas as pd df = pd.read_csv('people.csv') print(df.iloc[-1])Output python3 app.py Name Ruth Sex F Age 28 Height 65 Weight 131 Name: 17, dtype: objectSelect pandas rows using loc propertyPandas DataFrame loc property access a group of rows and columns by label(s) or a boolean array. DataFrame.loc[] is primarily label based, but may also be used with a boolean array.Allowed inputs are the following.A single label, e.g., 5 or `a', (note that 5 is interpreted as a label of the index, and never as an integer position along with the index).A list or array of labels, e.g. [`a', `b', `c'].A slice object with labels, e.g. `a':'f'.A boolean array of the same length as the axis being sliced, e.g., [True, False, True].A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).Now, in our example, we have not set an index yet. We can use the Pandas set_index() function to set the index. We are setting the Name column as our index.See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') df.set_index("Name", inplace=True)Now, we can select any label from the Name column in DataFrame to get the row for the particular label.Let's say we need to select a row that has label Gwen.See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') df.set_index("Name", inplace=True) print(df.loc['Gwen'])Outputpython3 app.py Sex F Age 26 Height 64 Weight 121 Name: Gwen, dtype: objectSelect Multiple rows of DataFrame in PandasPandas DataFrame loc[] property is used to select multiple rows of DataFrame.Let's stick with the above example and add one more label called Page and select multiple rows.So, we are selecting rows based on Gwen and Page labels.For selecting multiple rows, we have to pass the list of labels to the loc[] property.See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') df.set_index("Name", inplace=True) print(df.loc[['Gwen', 'Page']])Outputpython3 app.py Sex Age Height Weight Name Gwen F 26 64 121 Page F 31 67 135Boolean / Logical indexing using .locConditional selections with boolean arrays using data.loc[] is the most standard approach that I use with Pandas DataFrames. With boolean indexing or logical selection, you pass an array or Series of True/False values to the .loc indexer to select the rows where your Series has True values.See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') print(df.loc[df['Name'] == 'Bert'])Outputpython3 app.py Name Sex Age Height Weight 1 Bert M 42 68 166In the above example, the statement df[`Name'] == `Bert'] produces a Pandas Series with a True/False value for every row in the `df' DataFrame, where there are "True" values for the rows where the Name is "Bert".Here using a boolean True/False series to select rows in a pandas data frame ? all rows with the Name of "Bert" are selected.Python Pandas: Select rows based on conditionsLet's select all the rows where the age is equal or greater than 40.See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') print(df.loc[df['Age'] > 40])Outputpython3 app.py Name Sex Age Height Weight 0 Alex M 41 74 170 1 Bert M 42 68 166 8 Ivan M 53 72 175 10 Kate F 47 69 139Select rows where the Height is less than 65 and weight is greater than 160See the following code.# app.py import pandas as pd df = pd.read_csv('people.csv') op = df.loc[(df['Height'] > 70) & (df['Weight'] > 160)] print(op)Outputpython3 app.py Name Sex Age Height Weight 0 Alex M 41 74 170 3 Dave M 39 72 167 8 Ivan M 53 72 175 11 Luke M 34 72 163 16 Quin M 29 71 176ConclusionPandas DataFrame provides many properties like loc and iloc that are useful to select rows. In this tutorial, we have seen various boolean conditions to select rows, columns, and the particular values of the DataFrame.Finally, How to Select Rows from Pandas DataFrame tutorial is over. Starting here? This lesson is part of a full-length tutorial in using Python for Data Analysis. Check out the beginning. Goals of this lesson In this lesson, you'll learn how to use a DataFrame, a Python data structure that is similar to a database or spreadsheet table. You'll learn how to: The Python data analysis tools that you'll learn throughout this tutorial are very useful, but they become immensely valuable when they are applied to real data (and real problems). In this lesson, you'll be using tools from previous lesson, one of the go-to libraries for data manipulation, to conduct analysis of web traffic, which can help drive valuable decisions for a business. Pandas DataFrames Pandas has a few powerful data structures: A table with multiple columns is a DataFrame. A column of a DataFrame, or a list-like object, is a Series. A DataFrame is a table much like in SQL or Excel. It's similar in structure, too, making it possible to use similar operations such as aggregation, filtering, and pivoting. However, because DataFrames are built in Python, it's possible to use Python to program more advanced operations and manipulations than SQL and Excel can offer. As a bonus, the creators of pandas have focused on making the DataFrame operate very quickly, even over large datasets. DataFrames are particularly useful because powerful methods are built into them. In Python, methods are associated with objects, so you need your data to be in the DataFrame to use these methods. DataFrames can load data through a number of different data structures and files, including lists and dictionaries, csv files, excel files, and database records (more on that here). Loading data into a Mode Python Notebook Mode is an analytics platform that brings together a SQL editor, Python notebook, and data visualization builder. Throughout this tutorial, you can use Mode for free to practice writing and running Python code. For this lesson, you'll be using web traffic data from Watsi, an organization that allows people to fund healthcare costs for people around the world. To access the data, you'll need to use a bit of SQL. Here's how: Log into Mode or create an account. Navigate to this report and click Clone. This will take you to the SQL Query Editor, with a query and results pre-populated. Click Python Notebook under Notebook in the left navigation panel. This will open a new notebook, with the results of the query loaded in as a dataframe. The first input cell is automatically populated with datasets[0].head(n=5). Run this code so you can see the first five rows of the dataset. datasets is a list object. Nested inside this list is a DataFrame containing the results generated by the SQL query you wrote. To learn more about how to access SQL queries in Mode Python Notebooks, read this documentation. Now you're all ready to go. Prepping a DataFrame In Mode Python Notebooks, the first cell is automatically populated with the following code to access the data produced by the SQL query: datasets[0].head(n=5) The datasets object is a list, where each item is a DataFrame corresponding to one of the SQL queries in the Mode report. So datasets[0] is a dataframe object within the datasets list. You can see that the above command produces a table showing the first 5 rows of the results of your SQL query. Mode is able to do this because it comes pre-loaded with pandas. Still, you should get in the habit of giving librarias aliases so that you can refer to them easily throughout your code. Pandas is typically aliased as pd: You should also assign the DataFrame as a variable. Since you'll only be working with one DataFrame in this lesson, you can keep it simple and just call it data: data = datasets[0] # assign SQL query results to the data variable One final step before we're ready to start analysis: text cleanup. There are a few missing values in this dataset (in SQL you'd refer to them as null). For the sake of making this easier to look at, use the fillna() method to replace missing values with empty strings: data = data.fillna('') # replace missing values with strings for easier text processing About this dataset As mentioned above, in this lesson you'll be working with web traffic data from a nonprofit called Watsi. Every row in this dataset corresponds to a person visiting a page (this is known as a pageview). The general flow of pageviews is referred to as web traffic. Every pageview (row in the dataset) is composed of: 'referrer' The url that referred the user to the site (if available). For example, if someone arrived at the page through a Facebook link, referrer would be 'timestamp' The time the event occurred 'title' The title of the page the user visited on the Watsi website 'url' The url the user visited. For example, 'user_agent' The software the user used to accessed the site, including platform, browser, and extensions 'user_id' A unique id for each user (normally they'd be numbers--we've turned them into anonymous names instead) 'referrer_domain' The domain of the url that referred the user to the site. For example, "" 'website_section' The section of the website visited. For example, the section of is "team" 'platform' The device platform the user visited from. Possible values are "Desktop" and "Mobile" Get context for the data Through their website, Watsi enables direct funding of medical care. Take the time to understand what that looks like in practice. Visit some of the URLs you see in this dataset to familiarize yourself with the structure of the site and content, such as Mary's patient profile. Google Watsi and consider why people might engage with the service. Context is important - it'll help you make educated inferences in your analysis of the data. Data sampling This dataset contains 5,000 rows, which were sampled from a 500,000 row dataset spanning the same time period. Throughout these analyses, the number of events you count will be about 100 times smaller than they actually were, but the proportions of events will still generally be reflective of that larger dataset. In this case, a sample is fine because our purpose is to learn methods of data analysis with Python, not to create 100% accurate recommendations to Watsi. Selecting columns in a DataFrame As you learned in the previous lesson, you can select a value in a list or dictionary using brackets: cities[0] (gets item at place 0 in the list "cities") city_population['Tokyo'] (gets values associated with the key 'Tokyo' in the dictionary city_population) Similarly, you can use brackets to select a column in the DataFrame: 0 1 2 3 4 Name: url, dtype: object Selecting the column gives you access to the whole column, but will only show a preview. Below the column, the column name and data type (dtype) are printed for easy reference. The url column you got back has a list of numbers on the left. This is called the index, which uniquely identifies rows in the DataFrame. You will use the index to select individual rows, similar to how you selected rows from a list in an earlier lesson. A unique identifier is often necessary to refer to specific records in the dataset. For example, the DMV uses license plates to identify specific vehicles, instead of "Blue 1999 Honda Civic in California," which may or may not uniquely identify a car. Selecting columns will be important to much of the analysis you do throughout the tutorials, especially in grouping and counting events. Selecting rows in a DataFrame Selecting rows is useful for exploring the data and getting familiar with what values you might see. You can select rows by using brackets and row indexes. For example, you can select the first three rows of the DataFrame with the following code: referrer timestamp title url user_agent user_id referrer_domain website_section platform 0 2016-02-05 00:48:23 Watsi | Fund medical treatments for people aro... Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_4... CHAROLETTE S google Desktop 1 ... 2016-02-24 23:12:10 Watsi | The Meteor Chef Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... WARREN Q team Desktop 2 2015-12-25 17:59:35 Watsi | Give the gift of health with a Watsi G... Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1... MITCHEL O gift-cards Desktop The ":3" between the brackets effectively means "up to index 3". Similarly, you could select everything "from index 4 up to (but not including) index 7": referrer timestamp title url user_agent user_id referrer_domain website_section platform 4 2016-02-14 19:30:08 Watsi | Fund medical treatments for people aro... Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_2... ANDREE N Desktop 5 2015-10-15 06:04:40 Watsi | Fund a medical treatment on Watsi. 100... Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_4... SHAREN Y fund-treatments Desktop 6 2015-12-25 10:23:43 Watsi | Redeem your Watsi Gift Card Mozilla/5.0 (Windows NT 6.1; WOW64; rv:43.0) G... BRICE Z redeem Desktop Finally, you can select "everything from index 4997 onward": referrer timestamp title url user_agent user_id referrer_domain website_section platform 4997 ... 2016-01-03 02:48:38 Watsi | Fund medical treatments for people aro... Mozilla/5.0 (iPhone; CPU iPhone OS 7_0_4 like ... NOELLE P iPhone 4998 2016-02-07 23:47:53 Watsi | Success! Sarah from Kenya raised $1,12... Mozilla/5.0 (iPad; CPU OS 9_2 like Mac OS X) A... JERICA F profile iPad 4999 2015-11-17 16:38:25 Watsi | Fund a medical treatment on Watsi. 100... Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5... MARIANNA I fund-treatments Desktop Selecting a specific row To select a specific row, you must use the .ix method, with the row's index in brackets: referrer ... timestamp 2016-02-24 23:12:10 title Watsi | The Meteor Chef url user_agent Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... user_id WARREN Q referrer_domain website_section team platform Desktop Name: 1, dtype: object This is different from selecting columns. When selecting a column, you'll use data[], and when selecting a row, you'll use data.ix[]. Selecting rows and columns in a DataFrame Just as you can select from rows or columns, you can also select from both rows and columns at the same time. For example, you can select the first three rows of the title column by naming both the column and rows in square brackets: 0 Watsi | Fund medical treatments for people aro... 1 Watsi | The Meteor Chef 2 Watsi | Give the gift of health with a Watsi G... Name: title, dtype: object Think about this as listing the row and column selections one after another. Putting together a column selection and a row selection: data['title'] data[:3] You get the combined selection: data['title'][:3] The brackets selecting the column and selecting the rows are separate, and the selections are applied from left to right (in this last example, the column is selected, then it is filtered down to the first 3 rows). In fact, selecting the rows and then the column yields the same result: 0 Watsi | Fund medical treatments for people aro... 1 Watsi | The Meteor Chef 2 Watsi | Give the gift of health with a Watsi G... Name: title, dtype: object Select records from rows 10 to 15 in the 'referrer' column. View Solution There are at least two possible answers! data['referrer'][10:15] # select the column, then the rows 10 11 12 13 14 Name: referrer, dtype: object data[10:15]['referrer'] # select the rows, then the column 10 11 12 13 14 Name: referrer, dtype: object In this lesson, you learned to: Create a pandas DataFrame with data Select columns in a DataFrame Select rows in a DataFrame Select both columns AND rows in a DataFrame In the next lesson, you'll learn how to count values and plot a bar chart.

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