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Python - Processing CSV Data
Neha Kumawat
2 years ago
Table of Contents
- Introduction
- Input as CSV File
- Reading a CSV File
- Reading Specific Rows
- Reading Specific Columns
- Reading Specific Columns and Rows
- Reading Specific Columns for a Range of Rows
Introduction
Perusing data from CSV(comma isolated qualities) is a crucial need in Data Science. Frequently, we get information from different sources that can get traded to CSV design so they can be utilized by different frameworks. The Panadas library gives highlights utilizing which we can peruse the CSV record in full just as in parts for just a choice gathering of segments and lines.
Input as CSV File
The csv file is a text document in which the qualities in the sections are isolated by a comma. We should consider the accompanying information present in the document named input.csv.
You can make this record utilizing windows notebook by reordering this information. Spare the document as input.csv utilizing the spare As All files(*.*) alternative in the scratchpad.
id,name,salary,start_date,dept
1,Rick,623.3,2012-01-01,IT
2,Dan,515.2,2013-09-23,Operations
3,Tusar,611,2014-11-15,IT
4,Ryan,729,2014-05-11,HR
5,Gary,843.25,2015-03-27,Finance
6,Rasmi,578,2013-05-21,IT
7,Pranab,632.8,2013-07-30,Operations
8,Guru,722.5,2014-06-17,FinanceReading a CSV File
The read_csv function of the panda's library is utilized perused the substance of a CSV record into the python condition as a pandas DataFrame. The capacity can peruse the records from the OS by utilizing an appropriate way to the document.
import pandas as pd
data = pd.read_csv('path/input.csv')
print (data)At the point when we execute the above code, it delivers the accompanying outcome. If it's not too much trouble note how an extra section beginning with zero as a list has been made by the function.
id name salary start_date dept
0 1 Rick 623.30 2012-01-01 IT
1 2 Dan 515.20 2013-09-23 Operations
2 3 Tusar 611.00 2014-11-15 IT
3 4 Ryan 729.00 2014-05-11 HR
4 5 Gary 843.25 2015-03-27 Finance
5 6 Rasmi 578.00 2013-05-21 IT
6 7 Pranab 632.80 2013-07-30 Operations
7 8 Guru 722.50 2014-06-17 Finance
Reading Specific Rows
The read_csv function of the panda's library can likewise be utilized to peruse some particular lines for a given segment. We cut the outcome from the read_csv work utilizing the code that appeared beneath for the initial 5 lines for the segment named compensation.
import pandas as pd
data = pd.read_csv('path/input.csv')
# Slice the result for first 5 rows
print (data[0:5]['salary'])
At the point when we execute the above code, it creates the accompanying outcome.
0 623.30
1 515.20
2 611.00
3 729.00
4 843.25
Name: salary, dtype: float64
Reading Specific Columns
The read_csv function of the panda's library can likewise be utilized to peruse some particular segments. We utilize the multi-tomahawks ordering strategy called .loc() for this reason. We decide to show the compensation and name segment for all the lines.
import pandas as pd
data = pd.read_csv('path/input.csv')
# Use the multi-axes indexing funtion
print (data.loc[:,['salary','name']])
At the point when we execute the above code, it creates the accompanying outcome.
salary name
0 623.30 Rick
1 515.20 Dan
2 611.00 Tusar
3 729.00 Ryan
4 843.25 Gary
5 578.00 Rasmi
6 632.80 Pranab
7 722.50 Guru
Reading Specific Columns and Rows
The read_csv capacity of the panda's library can likewise be utilized to peruse some particular sections and explicit lines. We utilize the multi-tomahawks ordering strategy called .loc() for this reason. We decide to show the compensation and name segment for a portion of the lines.
import pandas as pd
data = pd.read_csv('path/input.csv')
# Use the multi-axes indexing funtion
print (data.loc[[1,3,5],['salary','name']])
At the point when we execute the above code, it delivers the accompanying outcome.
salary name
1 515.2 Dan
3 729.0 Ryan
5 578.0 Rasmi
Reading Specific Columns for a Range of Rows
The read_csv function of the panda's library can likewise be utilized to peruse some particular sections and scope of columns. We utilize the multi-tomahawks ordering strategy called .loc() for this reason. We decide to show the pay and name segment for a portion of the lines.
import pandas as pd
data = pd.read_csv('path/input.csv')
# Use the multi-axes indexing funtion
print (data.loc[2:6,['salary','name']])
At the point when we execute the above code, it creates the accompanying outcome.
salary name
2 611.00 Tusar
3 729.00 Ryan
4 843.25 Gary
5 578.00 Rasmi
6 632.80 Pranab
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