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Python Pandas - DataFrame
  • 时间：2024-11-05

Python Pandas - DataFrame

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A Data frame is a two-dimensional data structure, i.e., data is apgned 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.

Structure Table

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 −

Sr.No Parameter & Description
1

data

data takes various forms pke ndarray, series, map, psts, 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 DataFrame

A pandas DataFrame can be created using various inputs pke −

    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 pbrary and apasing 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 pst or a pst of psts.

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 pst 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 pst 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 pst 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

Spce 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 duppcated, then multiple rows will be dropped.

If you observe, in the above example, the labels are duppcate. 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.

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