Python Pandas DataFrame methods

`abs`	absolute value of elements
add	Adding to elements
add_prefix	Adding string to column name or row name
add_suffix	Adding string (at end )to column name or row name
agg	aggregating the data
align	Aligning two DataFrames using join methods
all	Returns True if all elements are True
any	Returns True if any element is True
append	Adding rows and columns
apply	Apply a function to DataFrame
applymap	Apply a function to each element

abs

Absolute value of elements

import pandas as pd 
my_dict=([-1,-1,2,-3])
my_data = pd.DataFrame(data=my_dict)
print(my_data.abs())

output

Absolute value of a column

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,40,-50],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
#print(my_data['MATH'].abs())
print(abs(my_data['MATH'])) # MATH column

Output

0    30
1    40
2    50
Name: MATH, dtype: int64

Absolute value of two columns

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,40,-50],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
print(abs(my_data[['MATH','ENGLISH']]))

Output

   MATH  ENGLISH
0    30       20
1    40       30
2    50       40

add

Adding to elements

import pandas as pd 
my_dict=([-1,-1,2,-3])
my_data = pd.DataFrame(data=my_dict)
print(my_data.add(2))

Output

Adding to a column

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,40,-50],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
#print(my_data[['MATH']].add(2)) # applying to single column
print(my_data[['MATH','ENGLISH']].add(2)) # applying to two columns

Output

   MATH  ENGLISH
0   -28       22
1    42      -28
2   -48       42

add_prefix

A string we can add before the row labels for a series and before the column names for a DataFrame

import pandas as pd 
my_dict=([1,2,3,4])
my_data = pd.Series(data=my_dict)
print(my_data.add_prefix('row_no_'))

Output for a series

row_no_0    1
row_no_1    2
row_no_2    3
dtype: int64

for a DataFrame

import pandas as pd 
my_dict=([1,2,3,4])
my_data = pd.DataFrame(data=my_dict)
print(my_data.add_prefix('col_no_'))

Output

   col_no_0
0         1
1         2
2         3
3         4

Using a column

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,40,-50],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
my_label=pd.DataFrame()
print(my_data[['NAME']].add_prefix('f_name '))

Output

  f_name NAME
0        Ravi
1        Raju
2        Alex

add_suffix

A string we can add after the row labels for a series and after the column names for a DataFrame

import pandas as pd 
my_dict=([1,2,3,4])
my_data = pd.Series(data=my_dict)
print(my_data.add_suffix('_no_'))

Output

0_no_    1
1_no_    2
2_no_    3
3_no_    4
dtype: int64

Using a DataFrame

import pandas as pd 
my_dict=([1,2,3,4])
my_data = pd.DataFrame(data=my_dict)
print(my_data.add_suffix(' col_no_'))

Output

   0 col_no_
0         1
1         2
2         3
3         4

Using column name

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,40,-50],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
my_label=pd.DataFrame()
print(my_data[['NAME']].add_suffix(' (f_name)'))

Output

  NAME (f_name)
0          Ravi
1          Raju
2          Alex

agg

Using Aggregate operations ( agg ) like sum, min, max, mean

import pandas as pd 
my_dict=([1,2,3,4])
my_data = pd.DataFrame(data=my_dict)
print(my_data.agg(['sum']))

Output

      0
sum  10

Using all

import pandas as pd 
my_dict=([1,2,3,4])
my_data = pd.DataFrame(data=my_dict)
print(my_data.agg(['sum','min','max','mean']))

Output

         0
sum   10.0
min    1.0
max    4.0
mean   2.5

Aggregate functions across multiple columns

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[30,40,50],
		 'ENGLISH':[20,30,40]}
my_data = pd.DataFrame(data=my_dict)
print(my_data.agg(['sum','min','max','mean'])) # all the columns

Output is here

              NAME   ID   MATH  ENGLISH
sum   RaviRajuAlex  6.0  120.0     90.0
min           Alex  1.0   30.0     20.0
max           Ravi  3.0   50.0     40.0
mean           NaN  2.0   40.0     30.0

Using only one column (MATH)

print(my_data['MATH'].agg(['sum','min','max','mean'])) # on one  columns

sum     120.0
min      30.0
max      50.0
mean     40.0
Name: MATH, dtype: float64

Using two columns ( MATH & ENGLISH)

print(my_data[['MATH','ENGLISH']].agg(['sum','min','max','mean'])) # two columns

Output

       MATH  ENGLISH
sum   120.0     90.0
min    30.0     20.0
max    50.0     40.0
mean   40.0     30.0

my_sale=sales.groupby(['product','store'],as_index=False).agg({'qty':'sum','price':'mean'})

Aggregate functions are used with GroupBy on multiple columns

align

It makes two DataFrame to match each other configuration.

import pandas as pd 
my_dict1={'NAME':['Ravi','Alex'],
         'MATH':[30,40]}
my_dict2={'NAME':['Ravi','Raju','Rone'],
         'ENGLISH':[20,50,60]}
my_data1 = pd.DataFrame(data=my_dict1)
my_data2 = pd.DataFrame(data=my_dict2)
a1, a2 = my_data1.align(my_data2, join='outer', axis=1)
print(a1)
print(a2)

Output

   ENGLISH  MATH  NAME
0      NaN    30  Ravi
1      NaN    40  Alex
   ENGLISH  MATH  NAME
0       20   NaN  Ravi
1       50   NaN  Raju
2       60   NaN  Rone

Using join='left'

import pandas as pd 
my_dict1={'NAME':['Ravi','Alex'],
          'ID':[1,2],
         'MATH':[30,40]}
my_dict2={'NAME':['Ravi','Raju','Rone'],
          'ID':[1,3,4],
         'ENGLISH':[20,50,60]}
my_data1 = pd.DataFrame(data=my_dict1)
my_data2 = pd.DataFrame(data=my_dict2)
a1, a2 = my_data1.align(my_data2, join='left', axis=1)
print(a1)
print(a2)

Output

   NAME  ID  MATH
0  Ravi   1    30
1  Alex   2    40
   NAME  ID  MATH
0  Ravi   1   NaN
1  Raju   3   NaN
2  Rone   4   NaN

all

Returns true if all elements are True , returns False if any one element is False , zero or empty

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,False,-50],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
my_data.all()

Output

NAME        True
ID          True
MATH       False
ENGLISH     True
dtype: bool

Using axis

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,False,-50],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
my_data.all(axis=1)

Output

0     True
1    False
2     True
dtype: bool

Using zero or empty element

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[-30,40,-50],
		 'ENGLISH':[20,'',40]}
my_data = pd.DataFrame(data=my_dict)
my_data.all(axis=1)

Output

0     True
1    False
2     True
dtype: bool

Using zero

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[0,40,-50],
		 'ENGLISH':[20,30,40]}
my_data = pd.DataFrame(data=my_dict)
my_data.all(axis=1)

Output

0    False
1     True
2     True
dtype: bool

any

Returns True if any one of the element is True. If all elements are False or zero or empty then False is returned.

import pandas as pd 
my_dict={'NAME':['']}
my_data = pd.DataFrame(data=my_dict)
my_data.any() # False

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','']}
my_data = pd.DataFrame(data=my_dict)
my_data.any() # True

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[0,0,0],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
my_data.any()

OUtput

NAME        True
ID          True
MATH       False
ENGLISH     True
dtype: bool

Using Axis

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[0,0,0],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
my_data.any(axis=1)

Output ( we are getting all True as Axis = 1 Horizontal , any one of the element is True

0    True
1    True
2    True
dtype: bool

When Axis =0, we can see all elements of Math is 0 ( or false )

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[0,0,0],
		 'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
my_data.any(axis=0)

Output

NAME        True
ID          True
MATH       False
ENGLISH     True
dtype: bool

append

Adds rows at the end , if columns are not matching then new column is added.

import pandas as pd 
my_dict1={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[10,20,30],
		 'ENGLISH':[20,30,40]}
my_dict2={'NAME':['Ravi2','Raju2','Alex2'],
         'ID':[1,2,3],'MATH':[0,0,0],
		 'ENGLISH':[40,50,60]}
my_data1 = pd.DataFrame(data=my_dict1)
my_data2 = pd.DataFrame(data=my_dict2)
my_data1= my_data1.append(my_data2,sort=True) 
print(my_data1)

Output

   ENGLISH  ID  MATH   NAME
0       20   1    10   Ravi
1       30   2    20   Raju
2       40   3    30   Alex
0       40   1     0  Ravi2
1       50   2     0  Raju2
2       60   3     0  Alex2

We can have common Index by setting ignore_index=True

my_data1= my_data1.append(my_data2,sort=True,ignore_index=True)

Output

  ENGLISH  ID  MATH   NAME
0       20   1    10   Ravi
1       30   2    20   Raju
2       40   3    30   Alex
3       40   1     0  Ravi2
4       50   2     0  Raju2
5       60   3     0  Alex2

verify_integrity = True , will raise ValueError while creating index with duplicates.
With different columns

import pandas as pd 
my_dict1={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[0,0,0],
		 'ENGLISH':[20,-30,40]}
my_dict2={'SCIENCE':[7,8,9]}
my_data1 = pd.DataFrame(data=my_dict1)
my_data2 = pd.DataFrame(data=my_dict2)
my_data1= my_data1.append(my_data2,sort=True) 
print(my_data1)

Output is here

   ENGLISH   ID  MATH  NAME  SCIENCE
0     20.0  1.0   0.0  Ravi      NaN
1    -30.0  2.0   0.0  Raju      NaN
2     40.0  3.0   0.0  Alex      NaN
0      NaN  NaN   NaN   NaN      7.0
1      NaN  NaN   NaN   NaN      8.0
2      NaN  NaN   NaN   NaN      9.0

In Pandas version 2.0.0 , append() is Removed deprecated , you will get this error
AttributeError: 'DataFrame' object has no attribute 'append'
Here use concat()

import pandas as pd 
my_dict1={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[10,20,30],
		 'ENGLISH':[20,30,40]}
my_dict2={'NAME':['Ravi2','Raju2','Alex2'],
         'ID':[1,2,3],'MATH':[0,0,0],
		 'ENGLISH':[40,50,60]}
my_data1 = pd.DataFrame(data=my_dict1)
my_data2 = pd.DataFrame(data=my_dict2)
#my_data1= my_data1.append(my_data2,sort=True) 
my_data1= pd.concat([my_data1,my_data2]) 
print(my_data1)

output

    NAME  ID  MATH  ENGLISH
0   Ravi   1    10       20
1   Raju   2    20       30
2   Alex   3    30       40
0  Ravi2   1     0       40
1  Raju2   2     0       50
2  Alex2   3     0       60

apply

The given function is applied across the Axis ( if given ) and returns a DataFrame as output

import pandas as pd 
my_dict={'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)

print(my_data.apply(sum)) # 30 
print(my_data.apply(max)) # 40

Using Axis

import pandas as pd 
my_dict={'ENGLISH':[20,-30,40],'MATH':[40,50,70]}
my_data = pd.DataFrame(data=my_dict)

print(my_data.apply(sum,axis=0))  
print(my_data.apply(sum,axis=1))

Output

ENGLISH     30
MATH       160
dtype: int64
0     60
1     20
2    110
dtype: int64

applying Numpy functions

import pandas as pd 
import numpy as my_np 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'ID':[1,2,3],'MATH':[0,0,0],
		 'ENGLISH':[20,-30,40]}

my_data = pd.DataFrame(data=my_dict)
print(my_data['ENGLISH'].apply(my_np.sqrt))

Output

0    4.472136
1         NaN
2    6.324555
Name: ENGLISH, dtype: float64

applymap

Applying function to each element

import pandas as pd 
my_dict={'ENGLISH':[20,-30,40]}
my_data = pd.DataFrame(data=my_dict)
print(my_data.applymap(lambda x:x+1))

Output

import pandas as pd 
my_dict={'ENGLISH':[20,-30,40],'MATH':[40,50,70]}
my_data = pd.DataFrame(data=my_dict)

print(my_data.applymap(lambda x:x+10))

Output

   ENGLISH  MATH
0       30    50
1      -20    60
2       50    80

Applying to a column

import pandas as pd 
my_dict={'ENGLISH':[20,-30,40],'MATH':[40,50,70]}
my_data = pd.DataFrame(data=my_dict)

print(my_data[['MATH']].applymap(lambda x:x+10))

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Pandas DataFrame Methods

abs

add

add_prefix

add_suffix

agg

align

all

any

append

apply

applymap

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