assignment 2

Python Pandas - DataFrame

A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.

pandas.DataFrame

A pandas DataFrame can be created using the following constructor −

pandas.DataFrame( data, index, columns, dtype, copy)

The parameters of the constructor are as follows −

Sr.No	Parameter & Description
1	data data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame.
2	index For the row labels, the Index to be used for the resulting frame is Optional Default np.arange(n) if no index is passed.
3	columns For column labels, the optional default syntax is - np.arange(n). This is only true if no index is passed.
4	dtype Data type of each column.
5	copy This command (or whatever it is) is used for copying of data, if the default is False.

Create an Empty DataFrame

A basic DataFrame, which can be created is an Empty Dataframe.

Example

#import the pandas library and aliasing as pd

import pandas as pd

df = pd.DataFrame()

print df

Create a DataFrame from Lists

The DataFrame can be created using a single list or a list of lists.

Example 1

import pandas as pd

data = [1,2,3,4,5]

df = pd.DataFrame(data)

print df

Example 2

import pandas as pd

data = [['Alex',10],['Bob',12],['Clarke',13]]

df = pd.DataFrame(data,columns=['Name','Age'])

print df

Example 3

import pandas as pd

data = [['Alex',10],['Bob',12],['Clarke',13]]

df = pd.DataFrame(data,columns=['Name','Age'],dtype=float)

print df

Create a DataFrame from Dict of ndarrays / Lists

All the ndarrays must be of same length. If index is passed, then the length of the index should equal to the length of the arrays.

If no index is passed, then by default, index will be range(n), where n is the array length.

Example 1

import pandas as pd

data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}

df = pd.DataFrame(data)

print df

Example 2

Let us now create an indexed DataFrame using arrays.

import pandas as pd

data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}

df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])

print df

Create a DataFrame from List of Dicts

List of Dictionaries can be passed as input data to create a DataFrame. The dictionary keys are by default taken as column names.

Example 1

The following example shows how to create a DataFrame by passing a list of dictionaries.

import pandas as pd

data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]

df = pd.DataFrame(data)

print df

Example 2

The following example shows how to create a DataFrame by passing a list of dictionaries and the row indices.

import pandas as pd

data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]

df = pd.DataFrame(data, index=['first', 'second'])

print df

Example 3

The following example shows how to create a DataFrame with a list of dictionaries, row indices, and column indices.

import pandas as pd

data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]

#With two column indices, values same as dictionary keys

df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b'])

#With two column indices with one index with other name

df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])

print df1

print df2

Create a DataFrame from Dict of Series

Dictionary of Series can be passed to form a DataFrame. The resultant index is the union of all the series indexes passed.

Example

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),

'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

print df

Column Selection

We will understand this by selecting a column from the DataFrame.

Example

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),

'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

print df ['one']

Column Addition

We will understand this by adding a new column to an existing data frame.

Example

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),

'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

# Adding a new column to an existing DataFrame object with column label by passing new series

print ("Adding a new column by passing as Series:")

df['three']=pd.Series([10,20,30],index=['a','b','c'])

print df

print ("Adding a new column using the existing columns in DataFrame:")

df['four']=df['one']+df['three']

print df

Column Deletion

Columns can be deleted or popped; let us take an example to understand how.

Example

# Using the previous DataFrame, we will delete a column

# using del function

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),

'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']),

'three' : pd.Series([10,20,30], index=['a','b','c'])}

df = pd.DataFrame(d)

print ("Our dataframe is:")

print df

# using del function

print ("Deleting the first column using DEL function:")

del df['one']

print df

# using pop function

print ("Deleting another column using POP function:")

df.pop('two')

print df

Row Selection, Addition, and Deletion

We will now understand row selection, addition and deletion through examples. Let us begin with the concept of selection.

Selection by Label

Rows can be selected by passing row label to a loc function.

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),

'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

print df.loc['b']

Selection by integer location

Rows can be selected by passing integer location to an iloc function.

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),

'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

print df.iloc[2]

Slice Rows

Multiple rows can be selected using ‘ : ’ operator.

import pandas as pd

d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),

'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

print df[2:4]

Addition of Rows

Add new rows to a DataFrame using the append function. This function will append the rows at the end.

import pandas as pd

df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])

df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])

df = df.append(df2)

print df

Deletion of Rows

Use index label to delete or drop rows from a DataFrame. If label is duplicated, then multiple rows will be dropped.

If you observe, in the above example, the labels are duplicate. Let us drop a label and will see how many rows will get dropped.

import pandas as pd

df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])

df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])

df = df.append(df2)

# Drop rows with label 0

df = df.drop(0)

print df