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

Features of DataFrame

• Potentially columns are of different types
• Size – Mutable
• Labeled axes (rows and columns)
• Can Perform Arithmetic operations on rows and columns

Structure

Let us assume that we are creating a data frame with student’s data.

You can think of it as an SQL table or a spreadsheet data representation.

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 −

S.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.arrange(n) if no index is passed.
3	columns For column labels, the optional default syntax is - np.arrange(n). This is only true if no index is passed.
4	dtype Data type of each column.
4	copy This command (or whatever it is) is used for copying of data, if the default is False.

Create DataFrame

A pandas DataFrame can be created using various inputs like −

• Lists
• dict
• Series
• Numpy ndarrays
• Another DataFrame

In the subsequent sections of this chapter, we will see how to create a DataFrame using these inputs.

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

Its output is as follows −

Empty DataFrame

Columns: []

Index: []

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

Its output is as follows −

0

0 1

1 2

2 3

3 4

4 5

Example 2

import pandas as pd

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

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

print df

Its output is as follows −

Name Age

0 Alex 10

1 Bob 12

2 Clarke 13

Example 3

import pandas as pd

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

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

print df

Its output is as follows −

Name Age

0 Alex 10.0

1 Bob 12.0

2 Clarke 13.0

Note − Observe, the dtype parameter changes the type of Age column to floating point.

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

Its output is as follows −

Age Name

0 28 Tom

1 34 Jack

2 29 Steve

3 42 Ricky

Note − Observe the values 0,1,2,3. They are the default index assigned to each using the function range(n).

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

Its output is as follows −

Age Name

rank1 28 Tom

rank2 34 Jack

rank3 29 Steve

rank4 42 Ricky

Note − Observe, the index parameter assigns an index to each row.

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

Its output is as follows −

a b c

0 1 2 NaN

1 5 10 20.0

Note − Observe, NaN (Not a Number) is appended in missing areas.

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

Its output is as follows −

a b c

first 1 2 NaN

second 5 10 20.0

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

Its output is as follows −

#df1 output

a b

first 1 2

second 5 10

#df2 output

a b1

first 1 NaN

second 5 NaN

Note − Observe, df2 DataFrame is created with a column index other than the dictionary key; thus, appended the NaN’s in place. Whereas, df1 is created with column indices same as dictionary keys, so NaN’s appended.

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

Its output is as follows −

one two

a 1.0 1

b 2.0 2

c 3.0 3

d NaN 4

Note − Observe, for the series one, there is no label ‘d’ passed, but in the result, for the d label, NaN is appended with NaN.

Let us now understand column selection, addition, and deletion through examples.

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']

Its output is as follows −

a 1.0

b 2.0

c 3.0

d NaN

Name: one, dtype: float64

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

Its output is as follows −

Adding a new column by passing as Series:

one two three

a 1.0 1 10.0

b 2.0 2 20.0

c 3.0 3 30.0

d NaN 4 NaN

Adding a new column using the existing columns in DataFrame:

one two three four

a 1.0 1 10.0 11.0

b 2.0 2 20.0 22.0

c 3.0 3 30.0 33.0

d NaN 4 NaN NaN

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

Its output is as follows −

Our dataframe is:

one three two

a 1.0 10.0 1

b 2.0 20.0 2

c 3.0 30.0 3

d NaN NaN 4

Deleting the first column using DEL function:

three two

a 10.0 1

b 20.0 2

c 30.0 3

d NaN 4

Deleting another column using POP function:

three

a 10.0

b 20.0

c 30.0

d NaN

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']

Its output is as follows −

one 2.0

two 2.0

Name: b, dtype: float64

The result is a series with labels as column names of the DataFrame. And, the Name of the series is the label with which it is retrieved.

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]

Its output is as follows −

one 3.0

two 3.0

Name: c, dtype: float64

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]

Its output is as follows −

one two

c 3.0 3

d NaN 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

Its output is as follows −

a b

0 1 2

1 3 4

0 5 6

1 7 8

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

Its output is as follows −

a b

1 3 4

1 7 8

In the above example, two rows were dropped because those two contain the same label 0.