Manipulate csv file using pandas in python3. See python code and output

import pandas as pd

Assume we have a file called data.csv

df = pd.read_csv('data.csv')
we print here as df.to_html(index=False).replace('\r\n','<br>')
All records - print(df)

Name	Age	Address	Education	City	Gender
John doe	26	23 Pauline blvd, Cherry Hill, NJ 08003	PHD	New York	Male
Mary Jay	34	24 Pauline blvd, Cherry Hill, NJ 08003	BSc	Philadelphia	Female
Eliz Gal	55	25 Pauline blvd, Cherry Hill, NJ 08003	BA	Mavern	Female
John Mar	35	26 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Female
John feb	29	27 Pauline blvd, Cherry Hill, NJ 08003	Diploma	New York	Male
John Apr	22	28 Pauline blvd, Cherry Hill, NJ 08003	MA	Philadelphia	Male
May Jun	65	29 Pauline blvd, Cherry Hill, NJ 08003	MBBS	Upper Darby	Male
John Dec	23	30 Pauline blvd, Cherry Hill, NJ 08003	BA	Poly	Male
John Nov	53	31 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John Joe	22	32 Pauline blvd, Cherry Hill, NJ 08003	MBA	Poly	Male
John Mard	12	33 Pauline blvd, Cherry Hill, NJ 08003	MCA	Poly	Male
John water	65	34 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John trac	32	35 Court blvd, New York, NY 08003	MBA	Poly	Male
John gan	65	36 State st, Philadelphia , PA 08003	MS	Poly	Male

First five rows print(df.head)

Name	Age	Address	Education	City	Gender
John doe	26	23 Pauline blvd, Cherry Hill, NJ 08003	PHD	New York	Male
Mary Jay	34	24 Pauline blvd, Cherry Hill, NJ 08003	BSc	Philadelphia	Female
Eliz Gal	55	25 Pauline blvd, Cherry Hill, NJ 08003	BA	Mavern	Female
John Mar	35	26 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Female
John feb	29	27 Pauline blvd, Cherry Hill, NJ 08003	Diploma	New York	Male

Last five rows print(df.tail)

Name	Age	Address	Education	City	Gender
John Joe	22	32 Pauline blvd, Cherry Hill, NJ 08003	MBA	Poly	Male
John Mard	12	33 Pauline blvd, Cherry Hill, NJ 08003	MCA	Poly	Male
John water	65	34 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John trac	32	35 Court blvd, New York, NY 08003	MBA	Poly	Male
John gan	65	36 State st, Philadelphia , PA 08003	MS	Poly	Male

All Adults above 25 years old print(df[df['age'] > 25])

Name	Age	Address	Education	City	Gender
John doe	26	23 Pauline blvd, Cherry Hill, NJ 08003	PHD	New York	Male
Mary Jay	34	24 Pauline blvd, Cherry Hill, NJ 08003	BSc	Philadelphia	Female
Eliz Gal	55	25 Pauline blvd, Cherry Hill, NJ 08003	BA	Mavern	Female
John Mar	35	26 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Female
John feb	29	27 Pauline blvd, Cherry Hill, NJ 08003	Diploma	New York	Male
May Jun	65	29 Pauline blvd, Cherry Hill, NJ 08003	MBBS	Upper Darby	Male
John Nov	53	31 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John water	65	34 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John trac	32	35 Court blvd, New York, NY 08003	MBA	Poly	Male
John gan	65	36 State st, Philadelphia , PA 08003	MS	Poly	Male

Descriptive statistics for an overview print(df.describe())

Age
14.000000
38.428571
18.391948
12.000000
23.750000
33.000000
54.500000
65.000000

Filter rows where age is between 25 and 30 and city is 'New York'

filtered_df = df[(df['Age'] > 25) & (df['Age'] < 30) & (df['City'] == 'New York')]

Name	Age	Address	Education	City	Gender
John doe	26	23 Pauline blvd, Cherry Hill, NJ 08003	PHD	New York	Male
John feb	29	27 Pauline blvd, Cherry Hill, NJ 08003	Diploma	New York	Male

Sort by age in ascending order

sorted_df = df.sort_values(by='Age')

Name	Age	Address	Education	City	Gender
John Mard	12	33 Pauline blvd, Cherry Hill, NJ 08003	MCA	Poly	Male
John Apr	22	28 Pauline blvd, Cherry Hill, NJ 08003	MA	Philadelphia	Male
John Joe	22	32 Pauline blvd, Cherry Hill, NJ 08003	MBA	Poly	Male
John Dec	23	30 Pauline blvd, Cherry Hill, NJ 08003	BA	Poly	Male
John doe	26	23 Pauline blvd, Cherry Hill, NJ 08003	PHD	New York	Male
John feb	29	27 Pauline blvd, Cherry Hill, NJ 08003	Diploma	New York	Male
John trac	32	35 Court blvd, New York, NY 08003	MBA	Poly	Male
Mary Jay	34	24 Pauline blvd, Cherry Hill, NJ 08003	BSc	Philadelphia	Female
John Mar	35	26 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Female
John Nov	53	31 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
Eliz Gal	55	25 Pauline blvd, Cherry Hill, NJ 08003	BA	Mavern	Female
May Jun	65	29 Pauline blvd, Cherry Hill, NJ 08003	MBBS	Upper Darby	Male
John water	65	34 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John gan	65	36 State st, Philadelphia , PA 08003	MS	Poly	Male

Or descending order

sorted_df_desc = df.sort_values(by='Age', ascending=False)

Name	Age	Address	Education	City	Gender
May Jun	65	29 Pauline blvd, Cherry Hill, NJ 08003	MBBS	Upper Darby	Male
John water	65	34 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John gan	65	36 State st, Philadelphia , PA 08003	MS	Poly	Male
Eliz Gal	55	25 Pauline blvd, Cherry Hill, NJ 08003	BA	Mavern	Female
John Nov	53	31 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John Mar	35	26 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Female
Mary Jay	34	24 Pauline blvd, Cherry Hill, NJ 08003	BSc	Philadelphia	Female
John trac	32	35 Court blvd, New York, NY 08003	MBA	Poly	Male
John feb	29	27 Pauline blvd, Cherry Hill, NJ 08003	Diploma	New York	Male
John doe	26	23 Pauline blvd, Cherry Hill, NJ 08003	PHD	New York	Male
John Dec	23	30 Pauline blvd, Cherry Hill, NJ 08003	BA	Poly	Male
John Apr	22	28 Pauline blvd, Cherry Hill, NJ 08003	MA	Philadelphia	Male
John Joe	22	32 Pauline blvd, Cherry Hill, NJ 08003	MBA	Poly	Male
John Mard	12	33 Pauline blvd, Cherry Hill, NJ 08003	MCA	Poly	Male

Sort by Gender sorted_gender_df = df.sort_values(by='Gender')

Name	Age	Address	Education	City	Gender
Mary Jay	34	24 Pauline blvd, Cherry Hill, NJ 08003	BSc	Philadelphia	Female
Eliz Gal	55	25 Pauline blvd, Cherry Hill, NJ 08003	BA	Mavern	Female
John Mar	35	26 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Female
John doe	26	23 Pauline blvd, Cherry Hill, NJ 08003	PHD	New York	Male
John feb	29	27 Pauline blvd, Cherry Hill, NJ 08003	Diploma	New York	Male
John Apr	22	28 Pauline blvd, Cherry Hill, NJ 08003	MA	Philadelphia	Male
May Jun	65	29 Pauline blvd, Cherry Hill, NJ 08003	MBBS	Upper Darby	Male
John Dec	23	30 Pauline blvd, Cherry Hill, NJ 08003	BA	Poly	Male
John Nov	53	31 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John Joe	22	32 Pauline blvd, Cherry Hill, NJ 08003	MBA	Poly	Male
John Mard	12	33 Pauline blvd, Cherry Hill, NJ 08003	MCA	Poly	Male
John water	65	34 Pauline blvd, Cherry Hill, NJ 08003	MA	Poly	Male
John trac	32	35 Court blvd, New York, NY 08003	MBA	Poly	Male
John gan	65	36 State st, Philadelphia , PA 08003	MS	Poly	Male

Transpose data for a different view
df_transposed = df.T
Pivot table for flexible analysis pivot_table = df.pivot_table(index='Gender', values='Age', aggfunc='mean')
Grouping is the start of data mining.
It is like grouping similar items and measuring their average weight. `groupby` adds depth to your analysis.

Age
41.333333
37.636364

Printed from Python list {'country':['Brzil'...]} with data_table = pd.DataFrame(data)

	country	capital	area	population
0	Brazil	Brasilia	8.516	200.40
1	Russia	Moscow	17.100	143.50
2	India	New Delhi	3.286	1252.00
3	China	Beijing	9.597	1357.00
4	South Africa	Pretoria	1.221	52.98