Introduction to Pandas Dataframe

Data frame is a main object in pandas. It is used to represent data with rows and columns. Data frame is a datastructure that represent the data in tabular or excel spread sheet.

Creating a dataframe

We can create a dataframe in multiple ways. The simplest way is by reading data from a csv file

import pandas as pd
df = pd.read_csv("weather_data.csv")   #read weather.csv data
df

	day	temperature	windspeed	event
0	1/1/2017	32	6	Rain
1	1/2/2017	35	7	Sunny
2	1/3/2017	28	2	Snow
3	1/4/2017	24	7	Snow
4	1/5/2017	32	4	Rain
5	1/6/2017	31	2	Sunny

If we have the data as a list of tuples, we can conver that to a dataframe.

#list of tuples

weather_data = [('1/1/2017', 32, 6, 'Rain'),
                ('1/2/2017', 35, 7, 'Sunny'),
                ('1/3/2017', 28, 2, 'Snow'),
                ('1/4/2017', 24, 7, 'Snow'),
                ('1/5/2017', 32, 4, 'Rain'),
                ('1/6/2017', 31, 2, 'Sunny')
               ]
df = pd.DataFrame(weather_data, columns=['day', 'temperature', 'windspeed', 'event'])
df

	day	temp	windspeed	event
0	1/1/2017	32	6	Rain
1	1/2/2017	35	7	Sunny
2	1/3/2017	28	2	Snow
3	1/4/2017	24	7	Snow
4	1/5/2017	32	4	Rain
5	1/6/2017	31	2	Sunny

To see the dimension of the dataframe we can use df.shape

df.shape

This will return (6,4) as these are 6 columns and 4 rows. The index is not counted in column.

We can use df.head to see the top 5 rows of the dataframe.

df.head()

	day	temperature	windspeed	event
0	1/1/2017	32	6	Rain
1	1/2/2017	35	7	Sunny
2	1/3/2017	28	2	Snow
3	1/4/2017	24	7	Snow
4	1/5/2017	32	4	Rain

Similar to df.head() there is a df.tail() which shows the last 5 elements

df.tail()

	day	temperature	windspeed	event
1	1/2/2017	35	7	Sunny
2	1/3/2017	28	2	Snow
3	1/4/2017	24	7	Snow
4	1/5/2017	32	4	Rain
5	1/6/2017	31	2	Sunny

We can see that index 0 is not present in the above table.

if we want to see the column header, we can use the df.columns

df.columns

Index(['day', 'temperature', 'windspeed', 'event'], dtype='object')

Slicing

df[2:5]

	day	temperature	windspeed	event
2	1/3/2017	28	2	Snow
3	1/4/2017	24	7	Snow
4	1/5/2017	32	4	Rain

This will return the rows from 2nd row till 4th(5-1) row

If we want to print a particular column, we can use the df.columnname

df.day      #print particular column data

0 1/1/2017 1 1/2/2017 2 1/3/2017 3 1/4/2017 4 1/5/2017 5 1/6/2017 Name: day, dtype: object

df['day'] #df.day (both are same)

This will also gets us the column day

We can print multiple rows using this method.

df[['day', 'event']]

	day	event
0	1/1/2017	Rain
1	1/2/2017	Sunny
2	1/3/2017	Snow
3	1/4/2017	Snow
4	1/5/2017	Rain
5	1/6/2017	Sunny