Before you begin, check to ensure you have a connection with the Python library in question. Once the connection is established, you can then push a query to Pandas. You need SQLite to establish a connection with your database, from where you will then create a DataFrame using the SELECT query as follows:
import sqlite3
con = sqlite3.connect("database.db")
Using our car dealership example from the previous posts, the SQL database will have a table denoted as sales , and the index. We can read from the database using the command below:
df = pd.read_sql_query("SELECT * FROM sales", con)
df
You will have the following output:
![](data:image/png;base64,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)
Just as we did with the CSV files, you can also bypass the index as follows:
df = df.set_index('index')
df
You will have the output below:
![](data:image/png;base64,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)
Once you are done with your data, you need to save it in a file system that is relevant to your needs. In Pandas, you can convert files to and from any of the file formats discussed above in the same way that you read the data files, when storing them as shown below:
df.to_csv('new_sales.csv')
df.to_sql('new_sales', con)
df.to_json('new_sales.json')
In data analysis, there are lots of methods that you can employ when using DataFrames, all of which are important to your analysis. Some operations are useful in performing simple data transformations, while others are necessary for complex statistical approaches.
In the examples below, we will use an example of a dataset from the English Premier League below:
squad_df = pd.read_csv("EPL-Data.csv", index_col="Teams")
As we load this dataset from the CSV file, we will use teams as our index. To view the data, you must first open a new dataset by printing out rows as follows:
squad_df.head()
You will have the following Output:
![](data:image/png;base64,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)
.head() will by default print the first five rows of your DataFrame. However, if you need more rows displayed, you can input a specific number to be printed as follows:
squad_df.head(7)
This will output the top seven rows as shown below:
![](data:image/png;base64,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)
In case you need to display only the last rows, use the .tail() syntax. You can also input a specific number. Assuming we want to determine the last three teams, we will use the syntax below:
squad_df.tail(3)
Our output will be as follows:
![](data:image/png;base64,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)
Generally, whenever you access any dataset, you will often access the first five rows to determine whether you are looking at the correct data set. From the display, you can see the index, column names, and the preset values. You will notice from the example above that the index for our DataFrame is the Teams column.
import sqlite3
con = sqlite3.connect("database.db")
Using our car dealership example from the previous posts, the SQL database will have a table denoted as sales , and the index. We can read from the database using the command below:
df = pd.read_sql_query("SELECT * FROM sales", con)
df
You will have the following output:
Just as we did with the CSV files, you can also bypass the index as follows:
df = df.set_index('index')
df
You will have the output below:
Once you are done with your data, you need to save it in a file system that is relevant to your needs. In Pandas, you can convert files to and from any of the file formats discussed above in the same way that you read the data files, when storing them as shown below:
df.to_csv('new_sales.csv')
df.to_sql('new_sales', con)
df.to_json('new_sales.json')
In data analysis, there are lots of methods that you can employ when using DataFrames, all of which are important to your analysis. Some operations are useful in performing simple data transformations, while others are necessary for complex statistical approaches.
In the examples below, we will use an example of a dataset from the English Premier League below:
squad_df = pd.read_csv("EPL-Data.csv", index_col="Teams")
As we load this dataset from the CSV file, we will use teams as our index. To view the data, you must first open a new dataset by printing out rows as follows:
squad_df.head()
You will have the following Output:
.head() will by default print the first five rows of your DataFrame. However, if you need more rows displayed, you can input a specific number to be printed as follows:
squad_df.head(7)
This will output the top seven rows as shown below:
In case you need to display only the last rows, use the .tail() syntax. You can also input a specific number. Assuming we want to determine the last three teams, we will use the syntax below:
squad_df.tail(3)
Our output will be as follows:
Generally, whenever you access any dataset, you will often access the first five rows to determine whether you are looking at the correct data set. From the display, you can see the index, column names, and the preset values. You will notice from the example above that the index for our DataFrame is the Teams column.
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