Business Benefits
Use Pandas to manipulate data and create data visualizations.
Load data from CSV files to the Pandas dataframe using the pandas.read_csv() function.
For example, if you have a CSV file with headers of name, physics, chemistry and algebra, and names and exam scores filled in as rows, you can call the CSV file using the pandas library and output the sheet using pandas:
import pandas as pd
import plotly.offline as pyo
import plotly.graph_objs as go
df = pd.read_csv(r’ /
name physics chemistry algebra
0 Somu 68 84 78
1 Kiku 74 56 88
2 Amol 77 73 82
3 Lini 78 69 87
## Read and output data from a CSV file using the `read_csv()` function with a delimiter argument.
For example, if you have a CSV file with headers `name`, `physics`, `chemistry` and `algebra`, student names and scores as rows, and each piece of data separated by a space, you can access its data using:
import pandas as pd
#load dataframe from csv
df = pd.read_csv('data.csv', delimiter=' ')
#print dataframe
print(df)
name physics chemistry algebra
0 Somu 68 84 78
1 Kiku 74 56 88
2 Amol 77 73 82
3 Lini 78 69 87
## Use the `header=None` method to output data when the CSV file you are using does not have column header.
`header=None` instructs the `pandas.read_csv()` function to ignore the first row in the CSV file - where the header would usually be - and print all the rows as data. For example, this code allows the data in `data.csv` to be printed, despite the file not containing a header:
import pandas as pd
# using header argument
df = pd.read_csv('data.csv', header=None)
## Output only the data from certain columns in a CSV file using the `names=` method.
For example, if the `data.csv` file has columns `first_name`, `last_name`, and `email_address`, and you need to use the `email_address` column data for processing, use this code to output the `email_address` column data into the pandas dataframe:
## Use the DataFrame library command `pd.read_excel` to access the contents of an Excel file and display the data as an array.
For example, if you want to access the contents of the file `tmp.xlsx`, which contains columns ***Name*** and ***Value***, use the `pd.read_excel` function to display the data as an array:
import pandas as pd
pd.read_excel('tmp.xlsx', index_col=0)
Output:
Name Value
0 string1 1
1 string2 2
## Access specific sheets from multiple sheets within an Excel file using the syntax `pd.read_excel('[filename]', sheet="[sheet]")`.
For example, if you want to output only the data from a specific sheet `cxl` within an Excel file named `tmp.xlsx`, use this code to read it:
import pandas as pd
pd.read_excel(‘tmp.xlsx’, sheet=“cxl”)
## Write data into an Excel file using the `to_excel` function.
For example, to create an Excel file named `cxl-lesson.xlsx` with two sheets named `Sheet1` and `Sheet2`, then reopen the file to add another sheet named `Sheet3`:
>>> with ExcelWriter('path_to_file.xlsx') as writer:
... df.to_excel(writer)
To write to separate sheets in a single file:
>>> with ExcelWriter('cxl-lesson.xlsx') as writer:
... df1.to_excel(writer, sheet_name='Sheet1')
... df2.to_excel(writer, sheet_name='Sheet2')
You can set the date format or datetime format:
>>> with ExcelWriter('cxl-lesson.xlsx',
... date_format='YYYY-MM-DD',
... datetime_format='YYYY-MM-DD HH:MM:SS') as writer:
... df.to_excel(writer)
You can also append to an existing Excel file:
>>> with ExcelWriter('cxl-lesson.xlsx', mode='a') as writer:
... df.to_excel(writer, sheet_name='Sheet3')