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Python Pandas - DataFrame

A DataFrame in Python's pandas library is a two-dimensional labeled data structure that is used for data manipulation and analysis. It can handle different data types such as integers, floats, and strings. Each column has a unique label, and each row is labeled with a unique index value, which helps in accessing specific rows.

DataFrame is used in machine learning tasks which allow the users to manipulate and analyze the data sets in large size. It supports the operations such as filtering, sorting, merging, grouping and transforming data.

Following are the features of the Pandas DataFrame −

• Columns can be of different types.
• Size is mutable.
• Labeled axes (rows and columns).
• Can Perform Arithmetic operations on rows and columns.

You can think of a DataFrame as similar to an SQL table or a spreadsheet data representation. Let us assume that we are creating a data frame with student's data.

A pandas DataFrame can be created using the following constructor −

pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None)

The parameters of the constructor are as follows −

data

data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame.

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.

columns

This parameter specifies the column labels, the optional default syntax is - np.arange(n). This is only true if no index is passed.

dtype

Data type of each column.

copy

This command (or whatever it is) is used for copying of data, if the default is False.

A pandas DataFrame can be created using various inputs like −

• Lists
• Dictionary
• Series
• Numpy ndarrays
• Another DataFrame
• External input iles like CSV, JSON, HTML, Excel sheet, and more.

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

An empty DataFrame can be created using the DataFrame constructor without any input.

Following is the example creating an empty DataFrame.

#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: []

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

The following example demonstrates how to create a pandas DataFrame from a Python list object.

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

Here is another example of creating a Pandas DataFrame from the Python list of list.

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

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.

Here is the example of creating the DataFrame from a Python dictionary.

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).

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.

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

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.

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 NaNs in place. Whereas, df1 is created with column indices same as dictionary keys, so NaNs appended.

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

Here is the 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.

Another example of creating a Pandas DataFrame from a Series −

import pandas as pd

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

df = pd.DataFrame(data)
print(df)

Its output is as follows −

       0
a      1
b      2
c      3
d      4

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