Pandas Write CSV File | Mastering in Python Pandas Library
Write csv file means to do some operations for data preprocessing or data cleaning.Data preprocessing is a data mining technique that involves transforming raw data into an understandable format.
How to Write CSV File in Python
Here we will discuss about pentameters of pd.read_csv function
import pandas as pd
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv')
df
Output >>>
ID Name Industry Inception Revenue Expenses Profit Growth
0 1 Lamtone IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
1 2 Stripfind Financial 2010 $12,329,371 916,455 Dollars 11412916 20%
2 3 Canecorporation Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
3 4 Mattouch IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
4 5 Techdrill Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
5 6 Techline Health 2006 $13,898,119 5,470,303 Dollars 8427816 23%
6 7 Cityace Health 2010 $9,254,614 6,249,498 Dollars 3005116 6%
7 8 Kayelectro Health 2009 $9,451,943 3,878,113 Dollars 5573830 4%
8 9 Ganzlax IT Services 2011 $14,001,180 3,878,153 Dollars 11901180 18%
9 10 Trantraxlax Government Services 2011 $11,088,336 5,635,276 Dollars 5453060 7%
To know the type of the dataset use type function
type(df)
Output >>> pandas.core.frame.DataFrame
This dataset is dataframe type
To know all the columns name
df.columns
Output >>> Index(['ID', 'Name', 'Industry', 'Inception', 'Revenue', 'Expenses', 'Profit',
'Growth'],
dtype='object')
If you want to read some specific rows of the dataset use nrows parameters
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', nrows = 1)
df
Output >>>
ID Name Industry Inception Revenue Expenses Profit Growth
0 1 Lamtone IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', nrows = 5)
df
Output >>>
ID Name Industry Inception Revenue Expenses Profit Growth
0 1 Lamtone IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
1 2 Stripfind Financial 2010 $12,329,371 916,455 Dollars 11412916 20%
2 3 Canecorporation Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
3 4 Mattouch IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
4 5 Techdrill Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', usecols = [0])
df
Output >>>
ID
0 1
1 2
2 3
3 4
4 5
5 6
6 7
7 8
8 9
9 10
df2 = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', usecols = [0,1])
df2
Output >>>
ID Name
0 1 Lamtone
1 2 Stripfind
2 3 Canecorporation
3 4 Mattouch
4 5 Techdrill
5 6 Techline
6 7 Cityace
7 8 Kayelectronics
8 9 Ganzlax
9 10 Trantraxlax
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', usecols = [1,2])
df
Output >>>
Name Industry
0 Lamtone IT Services
1 Stripfind Financial Services
2 Canecorporation Health
3 Mattouch IT Services
4 Techdrill Health
5 Techline Health
6 Cityace Health
7 Kayelectronics Health
8 Ganzlax IT Services
9 Trantraxlax Government Services
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', usecols = [2,4,7])
df
Output >>>
Industry Revenue Profit
0 IT Services $11,757,018 5274553
1 Financial Services $12,329,371 11412916
2 Health $10,597,009 3005820
3 IT Services $14,026,934 6597557
4 Health $10,573,990 3138627
5 Health $13,898,119 8427816
6 Health $9,254,614 3005116
7 Health $9,451,943 5573830
8 IT Services $14,001,180 11901180
9 Government Services $11,088,336 5453060
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv')
df
Output >>>
0 1 2 3 4 5 6 7
ID Name Industry Inception Revenue Expenses Profit Growth
0 1 Lamtone IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
1 2 Stripfind Financial 2010 $12,329,371 916,455 Dollars 11412916 20%
2 3 Canecorporation Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
3 4 Mattouch IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
4 5 Techdrill Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
5 6 Techline Health 2006 $13,898,119 5,470,303 Dollars 8427816 23%
6 7 Cityace Health 2010 $9,254,614 6,249,498 Dollars 3005116 6%
7 8 Kayelectro Health 2009 $9,451,943 3,878,113 Dollars 5573830 4%
8 9 Ganzlax IT Services 2011 $14,001,180 3,878,153 Dollars 11901180 18%
9 10 Trantraxlax Government Services 2011 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', skiprows = 1)
df
Output >>>
ID Name Industry Inception Employees Revenue Expenses Profit Growth
0 1 Lamtone IT Services 2009 55 $11,757,018 6,482,465 Dollars 5274553 30%
1 2 Stripfind Financial 2010 25 $12,329,371 916,455 Dollars 11412916 20%
2 3 Canecorporation Health 2012 6 $10,597,009 7,591,189 Dollars 3005820 7%
3 4 Mattouch IT Services 2013 6 $14,026,934 7,429,377 Dollars 6597557 26%
4 5 Techdrill Health 2009 9 $10,573,990 7,435,363 Dollars 3138627 8%
5 6 Techline Health 2006 65 $13,898,119 5,470,303 Dollars 8427816 23%
6 7 Cityace Health 2010 25 $9,254,614 6,249,498 Dollars 3005116 6%
7 8 Kayelectro Health 2009 687 $9,451,943 3,878,113 Dollars 5573830 4%
8 9 Ganzlax IT Services 2011 75 $14,001,180 3,878,153 Dollars 11901180 18%
9 10 Trantraxlax Government Services 2011 35 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', skiprows = 2)
df
Output >>>
1 Lamtone IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
0 2 Stripfind Financial Services 2010 $12,329,371 916,455 Dollars 11412916 20%
1 3 Canecorporation Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
2 4 Mattouch IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
3 5 Techdrill Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
4 6 Techline Health 2006 $13,898,119 5,470,303 Dollars 8427816 23%
5 7 Cityace Health 2010 $9,254,614 6,249,498 Dollars 3005116 6%
6 8 Kayelectronics Health 2009 $9,451,943 3,878,113 Dollars 5573830 4%
7 9 Ganzlax IT Services 2011 $14,001,180 3,878,113 Dollars 11901180 18%
8 10 Trantraxlax Government Services 2011 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', skiprows = 0)
df
Output >>>
0 1 2 3 4 5 6 7 8
0 ID Name Industry Inception Employees Revenue Expenses Profit Growth
1 1 Lamtone IT Services 2009 55 $11,757,018 6,482,465 Dollars 5274553 30%
2 2 Stripfind Financial 2010 25 $12,329,371 916,455 Dollars 11412916 20%
3 3 Canecorporation Health 2012 6 $10,597,009 7,591,189 Dollars 3005820 7%
4 4 Mattouch IT Services 2013 6 $14,026,934 7,429,377 Dollars 6597557 26%
5 5 Techdrill Health 2009 9 $10,573,990 7,435,363 Dollars 3138627 8%
6 6 Techline Health 2006 65 $13,898,119 5,470,303 Dollars 8427816 23%
7 7 Cityace Health 2010 25 $9,254,614 6,249,498 Dollars 3005116 6%
8 8 Kayelectro Health 2009 687 $9,451,943 3,878,113 Dollars 5573830 4%
9 9 Ganzlax IT Services 2011 75 $14,001,180 3,878,153 Dollars 11901180 18%
10 10 Trantraxlax Government Services 2011 35 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', skiprows = [0])
df
Output >>>
ID Name Industry Inception Employees Revenue Expenses Profit Growth
0 1 Lamtone IT Services 2009 55 $11,757,018 6,482,465 Dollars 5274553 30%
1 2 Stripfind Financial 2010 25 $12,329,371 916,455 Dollars 11412916 20%
2 3 Canecorporation Health 2012 6 $10,597,009 7,591,189 Dollars 3005820 7%
3 4 Mattouch IT Services 2013 6 $14,026,934 7,429,377 Dollars 6597557 26%
4 5 Techdrill Health 2009 9 $10,573,990 7,435,363 Dollars 3138627 8%
5 6 Techline Health 2006 65 $13,898,119 5,470,303 Dollars 8427816 23%
6 7 Cityace Health 2010 25 $9,254,614 6,249,498 Dollars 3005116 6%
7 8 Kayelectro Health 2009 687 $9,451,943 3,878,113 Dollars 5573830 4%
8 9 Ganzlax IT Services 2011 75 $14,001,180 3,878,153 Dollars 11901180 18%
9 10 Trantraxlax Government Services 2011 35 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', skiprows = [1])
df
Output >>>
0 1 2 3 4 5 6 7
0 1 Lamtone IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
1 2 Stripfind Financial Services 2010 $12,329,371 916,455 Dollars 11412916 20%
2 3 Canecorporation Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
3 4 Mattouch IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
4 5 Techdrill Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
5 6 Techline Health 2006 $13,898,119 5,470,303 Dollars 8427816 23%
6 7 Cityace Health 2010 $9,254,614 6,249,498 Dollars 3005116 6%
7 8 Kayelectronics Health 2009 $9,451,943 3,878,113 Dollars 5573830 4%
8 9 Ganzlax IT Services 2011 $14,001,180 3,878,113 Dollars 11901180 18%
9 10 Trantraxlax Government Services 2011 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', skiprows = [0,2,3])
df
Output >>>
ID Name Industry Inception Revenue Expenses Profit Growth
0 3 Canecorporation Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
1 4 Mattouch IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
2 5 Techdrill Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
3 6 Techline Health 2006 $13,898,119 5,470,303 Dollars 8427816 23%
4 7 Cityace Health 2010 $9,254,614 6,249,498 Dollars 3005116 6%
5 8 Kayelectronics Health 2009 $9,451,943 3,878,113 Dollars 5573830 4%
6 9 Ganzlax IT Services 2011 $14,001,180 3,878,113 Dollars 11901180 18%
7 10 Trantraxlax Government Services 2011 $11,088,336 5,635,276 Dollars 5453060 7%
df1 = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv')
df1
Output >>>
ID Name Industry Inception Revenue Expenses Profit Growth
0 1 Lamtone IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
1 2 Stripfind Financial 2010 $12,329,371 916,455 Dollars 11412916 20%
2 3 Canecorporation Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
3 4 Mattouch IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
4 5 Techdrill Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
5 6 Techline Health 2006 $13,898,119 5,470,303 Dollars 8427816 23%
6 7 Cityace Health 2010 $9,254,614 6,249,498 Dollars 3005116 6%
7 8 Kayelectro Health 2009 $9,451,943 3,878,113 Dollars 5573830 4%
8 9 Ganzlax IT Services 2011 $14,001,180 3,878,153 Dollars 11901180 18%
9 10 Trantraxlax Government Services 2011 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', index_col = 'ID')
df
Output >>>
Name Industry Inception Employees Revenue Expenses Profit Growth
ID
1 Lamtone IT Services 2009 55 $11,757,018 6,482,465 Dollars 5274553 30%
2 Stripfind Financial 2010 25 $12,329,371 916,455 Dollars 11412916 20%
3 Canecorporation Health 2012 6 $10,597,009 7,591,189 Dollars 3005820 7%
4 Mattouch IT Services 2013 6 $14,026,934 7,429,377 Dollars 6597557 26%
5 Techdrill Health 2009 9 $10,573,990 7,435,363 Dollars 3138627 8%
6 Techline Health 2006 65 $13,898,119 5,470,303 Dollars 8427816 23%
7 Cityace Health 2010 25 $9,254,614 6,249,498 Dollars 3005116 6%
8 Kayelectro Health 2009 687 $9,451,943 3,878,113 Dollars 5573830 4%
9 Ganzlax IT Services 2011 75 $14,001,180 3,878,153 Dollars 11901180 18%
10 Trantraxlax Government Services 2011 35 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', index_col = 0)
df
Output >>>
Name Industry Inception Employees Revenue Expenses Profit Growth
ID
1 Lamtone IT Services 2009 55 $11,757,018 6,482,465 Dollars 5274553 30%
2 Stripfind Financial 2010 25 $12,329,371 916,455 Dollars 11412916 20%
3 Canecorporation Health 2012 6 $10,597,009 7,591,189 Dollars 3005820 7%
4 Mattouch IT Services 2013 6 $14,026,934 7,429,377 Dollars 6597557 26%
5 Techdrill Health 2009 9 $10,573,990 7,435,363 Dollars 3138627 8%
6 Techline Health 2006 65 $13,898,119 5,470,303 Dollars 8427816 23%
7 Cityace Health 2010 25 $9,254,614 6,249,498 Dollars 3005116 6%
8 Kayelectro Health 2009 687 $9,451,943 3,878,113 Dollars 5573830 4%
9 Ganzlax IT Services 2011 75 $14,001,180 3,878,153 Dollars 11901180 18%
10 Trantraxlax Government Services 2011 35 $11,088,336 5,635,276 Dollars 5453060 7%
df = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', index_col = 'Name')
df
Output >>>
ID Industry Inception Revenue Expenses Profit Growth
Name
Lamtone 1 IT Services 2009 $11,757,018 6,482,465 Dollars 5274553 30%
Stripfind 2 Financial Services 2010 $12,329,371 916,455 Dollars 11412916 20%
Canecorporation 3 Health 2012 $10,597,009 7,591,189 Dollars 3005820 7%
Mattouch 4 IT Services 2013 $14,026,934 7,429,377 Dollars 6597557 26%
Techdrill 5 Health 2009 $10,573,990 7,435,363 Dollars 3138627 8%
Techline 6 Health 2006 $13,898,119 5,470,303 Dollars 8427816 23%
Cityace 7 Health 2010 $9,254,614 6,249,498 Dollars 3005116 6%
Kayelectronics 8 Health 2009 $9,451,943 3,878,113 Dollars 5573830 4%
Ganzlax 9 IT Services 2011 $14,001,180 3,878,113 Dollars 11901180 18%
Trantraxlax 10 Government Services 2011 $11,088,336 5,635,276 Dollars 5453060 7%
df1 = pd.read_csv('F:\\Machine Learning\\DataSet\\Fortune_10.csv', index_col = 2)
df1
Output >>>
ID Name Inception Revenue Expenses Profit Growth
Industry
IT Services 1 Lamtone 2009 $11,757,018 6,482,465 Dollars 5274553 30%
Financial Services 2 Stripfind 2010 $12,329,371 916,455 Dollars 11412916 20%
Health 3 Canecorporation 2012 $10,597,009 7,591,189 Dollars 3005820 7%
IT Services 4 Mattouch 2013 $14,026,934 7,429,377 Dollars 6597557 26%
Health 5 Techdrill 2009 $10,573,990 7,435,363 Dollars 3138627 8%
Health 6 Techline 2006 $13,898,119 5,470,303 Dollars 8427816 23%
Health 7 Cityace 2010 $9,254,614 6,249,498 Dollars 3005116 6%
Health 8 Kayelectronics 2009 $9,451,943 3,878,113 Dollars 5573830 4%
IT Services 9 Ganzlax 2011 $14,001,180 3,878,113 Dollars 11901180 18%
Government Services 10 Trantraxlax 2011 $11,088,336 5,635,276 Dollars 5453060 7%
To Download dataset click here – Fortune_10
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