Introduction
In this article, we will learn about pandas function of iterrows(), datareader() and operation for selecting rows by value. These are very useful for accessing data inside the dataframes while working with your machine learning and data science projects.
Importing Pandas Library
Let’s start the article by importing the Pandas library.
import pandas as pd
Pandas Iterrows : iterrows()
This function helps in iterating over Dataframe rows as (Index,Series) pairs. This is used for accessing the data contained in dataframes.
Syntax
DataFrame.iterrows()
The function returns index of the row. The data of the rows is obtained in the form of series. Lastly, we get a generator that iterates over the frame.
Example 1: Simple example of iterrows()
In this example of iterrows, we can access the data using this function.
df = pd.DataFrame([[7, 8.5]], columns=['float', 'int'])
Since iterrows() returns iterator, we can use next function to see the content of the iterator. We can see that iterrows returns a tuple with row index and row data as a Series object
row = next(df.iterrows())[1]
row
Example 2: Using iterrows with for loop
In this example, iterrows is used with “for” loop.
fruits = pd.DataFrame({'seed_count': [8, 25, 50, 1],
'quantity': [10, 23, 32, 45],
'water_content': [5, 20, 25, 30]},
index=['orange', 'watermelon', 'strawberry', 'mango'])
fruits
for x,y in fruits.iterrows():
if y['quantity'] in [10,45]:
y['quantity'] = 50
print(x,y)
Pandas Datareader : Datareader()
Datareader library of pandas is useful to those people who are looking to analyse stocks data of different countries. For some stocks, there is a requirement of an API key.
Example 1: Using datareader library to load stocks of Fred
The datareader library helps in fetching the data of different stocks of various countries.
import pandas_datareader.data as web
import datetime
After loading the library, we are specifying the dates for fetching desired stocks from the time range.
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
gdp = web.DataReader('GDP', 'fred', start, end)
gdp.loc['2013-01-01']
By using the datareader library, we can fetch the values of stocks of different variants.
inflation = web.DataReader(['CPIAUCSL', 'CPILFESL'], 'fred', start, end)
inflation.head()
Example 2: Fetching World Bank’s data
By using pandas datareader, we can also fetch the World Bank’s data. DataReader can also get the values of other stocks using API’s.
from pandas_datareader import wb
matches = wb.search('gdp.*capita.*const')
dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'CA', 'MX'], start=2012, end=2018)
print(dat)
Pandas Selecting rows by value
The pandas library gives us the ability to select rows from a dataframe based on the values present in it. For fetching these values, we can use different conditions.
Example 1: Selecting rows by value
This pandas operation helps us in selecting rows by filtering it through a condition of columns.
# importing pandas
import pandas as pd
record = { 'E_Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Section': ['Management', 'Marketing', 'Finance', 'Sales', 'Technical', 'HR'],
'Salary': [88, 92, 95, 70, 65, 78] }
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['E_Name', 'Age', 'Section', 'Salary'])
print(dataframe)
Here employees with salary greater than 80 are only selected.
# selecting rows based on condition
result = dataframe[dataframe['Salary'] > 80]
print(result)
Example 2: Selecting specific rows using pandas
In this example, employee/employees who do not have salary equal to 95 are selected.
# selecting rows based on condition
result = dataframe.loc[dataframe['Salary'] != 95]
print(result)
Conclusion
In this tutorial, we looked at pandas functions and libraries used for fetching data from different sources. The pandas function which we covered were namely pandas iterrows(), pandas in-built library datareader() and pandas operation of selection of rows using values. Through this tutorial, we looked at different examples of these functions and also tried to understand the details of the syntax.
- Also Read – Tutorial – Pandas Drop, Pandas Dropna, Pandas Drop Duplicate
- Also Read – Pandas Visualization Tutorial – Bar Plot, Histogram, Scatter Plot, Pie Chart
- Also Read – Tutorial – Pandas Concat, Pandas Append, Pandas Merge, Pandas Join
Reference – https://pandas.pydata.org/docs/
