Pandas Kütüphanesi Ders - 4

Lesson 4

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In this lesson were going to go back to the basics. We will be working with a small data set so that you can easily understand what I am trying to explain. We will be adding columns, deleting columns, and slicing the data many different ways. Enjoy!

In [1]:

# Import libraries
import pandas as pd
import sys

In [2]:

print('Python version ' + sys.version)
print('Pandas version: ' + pd.__version__)

Python version 3.5.1 |Anaconda custom (64-bit)| (default, Feb 16 2016, 09:49:46) [MSC v.1900 64 bit (AMD64)]
Pandas version: 0.20.1

In [3]:

# Our small data set
d = [0,1,2,3,4,5,6,7,8,9]

# Create dataframe
df = pd.DataFrame(d)
df

Out[3]:

	0
0	0
1	1
2	2
3	3
4	4
5	5
6	6
7	7
8	8
9	9

In [4]:

# Lets change the name of the column
df.columns = ['Rev']
df

Out[4]:

	Rev
0	0
1	1
2	2
3	3
4	4
5	5
6	6
7	7
8	8
9	9

In [5]:

# Lets add a column
df['NewCol'] = 5
df

Out[5]:

	Rev	NewCol
0	0	5
1	1	5
2	2	5
3	3	5
4	4	5
5	5	5
6	6	5
7	7	5
8	8	5
9	9	5

In [6]:

# Lets modify our new column
df['NewCol'] = df['NewCol'] + 1
df

Out[6]:

	Rev	NewCol
0	0	6
1	1	6
2	2	6
3	3	6
4	4	6
5	5	6
6	6	6
7	7	6
8	8	6
9	9	6

In [7]:

# We can delete columns
del df['NewCol']
df

Out[7]:

	Rev
0	0
1	1
2	2
3	3
4	4
5	5
6	6
7	7
8	8
9	9

In [8]:

# Lets add a couple of columns
df['test'] = 3
df['col'] = df['Rev']
df

Out[8]:

	Rev	test	col
0	0	3	0
1	1	3	1
2	2	3	2
3	3	3	3
4	4	3	4
5	5	3	5
6	6	3	6
7	7	3	7
8	8	3	8
9	9	3	9

In [9]:

# If we wanted, we could change the name of the index
i = ['a','b','c','d','e','f','g','h','i','j']
df.index = i
df

Out[9]:

	Rev	test	col
a	0	3	0
b	1	3	1
c	2	3	2
d	3	3	3
e	4	3	4
f	5	3	5
g	6	3	6
h	7	3	7
i	8	3	8
j	9	3	9

We can now start to select pieces of the dataframe using loc.

In [10]:

df.loc['a']

Out[10]:

Rev     0
test    3
col     0
Name: a, dtype: int64

In [11]:

# df.loc[inclusive:inclusive]
df.loc['a':'d']

Out[11]:

	Rev	test	col
a	0	3	0
b	1	3	1
c	2	3	2
d	3	3	3

In [12]:

# df.iloc[inclusive:exclusive]
# Note: .iloc is strictly integer position based. It is available from [version 0.11.0] (http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-11-0-april-22-2013) 
df.iloc[0:3]

Out[12]:

	Rev	test	col
a	0	3	0
b	1	3	1
c	2	3	2

We can also select using the column name.

In [13]:

df['Rev']

Out[13]:

a    0
b    1
c    2
d    3
e    4
f    5
g    6
h    7
i    8
j    9
Name: Rev, dtype: int64

In [14]:

df[['Rev', 'test']]

Out[14]:

	Rev	test
a	0	3
b	1	3
c	2	3
d	3	3
e	4	3
f	5	3
g	6	3
h	7	3
i	8	3
j	9	3

In [15]:

# df.ix[rows,columns]
# replaces the deprecated ix function
#df.ix[0:3,'Rev']
df.loc[df.index[0:3],'Rev']

Out[15]:

a    0
b    1
c    2
Name: Rev, dtype: int64

In [16]:

# replaces the deprecated ix function
#df.ix[5:,'col']
df.loc[df.index[5:],'col']

Out[16]:

f    5
g    6
h    7
i    8
j    9
Name: col, dtype: int64

In [17]:

# replaces the deprecated ix function
#df.ix[:3,['col', 'test']]
df.loc[df.index[:3],['col', 'test']]

Out[17]:

	col	test
a	0	3
b	1	3
c	2	3

There is also some handy function to select the top and bottom records of a dataframe.

In [18]:

# Select top N number of records (default = 5)
df.head()

Out[18]:

	Rev	test	col
a	0	3	0
b	1	3	1
c	2	3	2
d	3	3	3
e	4	3	4

In [19]:

# Select bottom N number of records (default = 5)
df.tail()

Out[19]:

	Rev	test	col
f	5	3	5
g	6	3	6
h	7	3	7
i	8	3	8
j	9	3	9

This tutorial was created by HEDARO