Ready to learn how to analyze data with Python in few minutes, without knowing too much about Python language? You can easily import 130.000 rows in few sceonds with Pandas module for Python. And using less than 10 commands you can explore number of records, column, and start to know mean, max & minimum and a lot more on your dataset
pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Let’see the code to import CSV. Less than a minute
import pandas as pd #Import module pandas #Using Panda to load csv Location = r'C:\DATASET\WINE REVIEWS\winemag-data-130k-v2.csv' df = pd.read_csv(Location) #Read CSV in location
Attention: change your path, changing ‘C:\DATASET\WINE REVIEWS\winemag-data-130k-v2.csv’ with your path. If you are not familiar with this check it out: 3 simple way to change your path in Anaconda/Python
WHAT KIND OF VARIABLE WE HAVE IN THE DATASET?
Using Anaconda, analyzing data with Python and Panda will be simple. We can see that now our df (Dataframe) has around 130k records and 14 variables(129971,14)
Using command:
df.dtypes
you can easily check what kind of data do you have. Here we have basically all variable as an object , while first variable, points are integer number (int64) and price include floating number (with decimals)
Out[4]: Unnamed: 0 int64 country object description object designation object points int64 price float64 province object region_1 object region_2 object taster_name object taster_twitter_handle object title object variety object winery object dtype: object
If you want to learn more about which are most common Statistical & Math skills, discover our dedicated page.
LEARN MAIN STATISTICS WITH ONE COMMAND:DF.DESCRIBE
Let’s focus only only on numerical variables. Point: This represent a number from 0 to 100 as scoring of wine taster. Price, no need to explain
We can see if we digit:
df.describe
We can see that variable Points are available for all data (129.971) and has a minimum of 80 and a max of 100, with an average of 88,45
Our first 25% of dataset (32.492) has an average point of 86, 50% of dataset (88)
Of course you can do the same in Excel, but you need to create several cell and write several formulas. So Python will help you here to save some time
UNDERSTAND IF YOU HAVE MISSING VALUES IN YOUR DATASET
Another powerful command to analyze data with python is understanding the quality of your dataset. Do you have some missing value? How many? In which variables?
Also here do you see the value of using python instead
Write these two lines of code and you will find how many missing values you have in your dataset for every columns
null_columns=df.columns[df.isnull().any()] df[null_columns].isnull().sum()
WHICH IS THE AVERAGE SCORING AND PRICE IN ANY COUNTRY?
One of the most commont things to analyze data with Python, is to understand average data, maybe grouping for some of your variables.If you want to know which is the average score and price by country, you can use
.groupby ().operations,
where in the parenthesis you need to put variable to be grouped and after the operation that you want to do .mean or .sum for example
df.groupby(‘country’).mean()
So we will discover that in our dataset, average price in Argentina is 24,5$ with an average of 86,7$, beter than Austria that has 90 point in average but you have to pay 30$
Of course you can groupby by multiple column (‘country’,’region’)
FILTER ONLY DATA WITH PRICE >90$
Maybe you are interesting to easily know how many permutations you have in your database that fit with a particular threshold. In this case we would like to know how many records has a price >90$. Result is more than 4.000 records
df[df.price>90]
HOW TO EXPORT IN CSV OR EXCEL WHEN YOU ANALYZE DATA WITH PYTHON ?
Easy, just write
df.to_csv('first.csv') #creating a csv file called first
df.to_excel('first.xlsx', sheet_name='Sheet1') #creating a xlsx file called first, in sheet 1
OTHER USEFUL COMMAND TO ANALYZE DATA WITH PYTHON
Df.head= See head of your dataset
Df.columns= show your columns names
Df.tail[3] = show latest 3 record of your dataset
Df.index= show you the range of your dataset
Stay tuned next time we will describe how to user wordcloud to describe string variables. Subscribe to our newsletter for more news
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