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Pandas Introduction

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Pandas Some functions and properties

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Pandas math related functions

Let's see the dataset. This dataset is will be used in the upcoming example of code.

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
print(df)
Output
Id Name Group_name Total_marks Grade Ranking
0 01 A Science 700 A+ 01
1 02 B Commerce 618 B+ 02
2 03 A Science 700 A+ 01
3 04 D Arts 687 A+ 01
4 05 E Commerce 611 B+ 02
5 06 F Arts 599 C+ 03
6 07 P Science 575 C+ 03
7 08 F Arts 600 C 03
8 09 I Commerce 550 C+ 03
9 10 J Science 650 A+ 01
10 11 K Arts 680 A+ 01
11 12 L Science 570 C+ 03
12 13 M Arts 599 C+ 03
13 14 N Commerce 597 C+ 03
14 15 O Science 697 A+ 01
15 16 B Arts 570 C+ 03
16 17 D Science 588 C+ 03
17 18 E Science 687 A+ 01
18 19 C Commerce 688 A+ 01
19 20 P Arts 588 C+ 03
20 21 C Science 619 B+ 02
21 22 M Commerce 600 B+ 02
22 23 P Arts 700 A+ 01

sum() function

The sum () function is used to get the sum of the numeric column.

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df.Total_marks.sum()
print(df1)
Output
 14473

max() function

The max() function is used to get the maximum value of the numeric column.

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df.Total_marks.max()
print(df1)
Output
 700

min() function

The min() function is used to get the minimum value of the numeric column.

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df.Total_marks.min()
print(df1)
Output
 550

mean() function

The mean() function is used to get the mean value of numeric columns.

Example 1:

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df.mean()
print(df1)
Output
 Id    12.000000
Total_marks    629.260870
Ranking    .043478
dtype: float64

Example 2:

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df.mean()
print(df1)
Output
 629.2608695652174

median() function

The median() function is used to get the median value of numeric columns.

Example 1:

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df.median()
print(df1)
Output
 Id    12.0
Total marks    600.0
Ranking    2.0
dtype: float64

Example 2:

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df["Total marks"].median()
print(df1)
Output
 600.0

mode() function

The mode() function is used to get the mode value of the categorical column.

Input
 import pandas as pd
df=pd.read_csv("D:\\CSV Datasets for practice\\practice.csv")
df1=df.Name.mode()
print(df1)
Output
 0   Science
dtype: object

count() function

The count() function is used to get the count of numeric columns.

Example 1:

Input
 import pandas as pd
df=pd.read_csv("D:\\CSV Datasets for practice\\practice.csv")
df1=df.count()
print(df1)
Output
 Id        23
Name         23
Group_name     23
Total marks     23
Grade         23
Ranking         23
Date         23 dtype: int64

Example 2:

Input
 import pandas as pd
df=pd.read_csv("D:\\CSV Datasets for practice\\practice.csv")
df1=df["Total marks"].count()
print(df1)
Output
 23

var() function

The var() function is used to get the var of numeric columns.

Example 1:

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df.var()
print(df1)
Output
 Id     46.000000
Total marks     2703.703557
Ranking     0.861660
dtype: float64

Example 2:

Input
 import pandas as pd
df=pd.read_csv("practice.csv")
df1=df["Total marks"].var()
print(df1)
Output
 2703.7035573122535

std() function

Std() function is used to get the mean value of numeric columns.

Example 1:

Input
 import pandas as pd
df=pd.read_csv("D:\\CSV Datasets for practice\\practice.csv")
df1=df.std()
print(df1)
Output
 Id     6.782330
Total_marks  51.136375
Ranking     0.928256
dtype: float64

Example 2:

Input
 import pandas as pd
df=pd.read_csv("D:\\CSV Datasets for practice\\practice.csv")
df1=df.std()
print(df1)
Output
 51.13637505489552

aggregate() function

Using aggregate() function we can apply some aggregation like sum, max, mix etc across one or more column. Aggregation only can be applyed in numerical column.

Syntax:
 DataFrame.aggregate(func, axis=0, *args, **kwargs)
Here,
function= string, dictionary, or list of string/callables.
axis= value 0(row) or 1(column)

Let's aggregate sum and max function across all the columns in data frame.

In the dataset we have numerical and categorical columns. We know that aggregation only applied in numerical column. Because we are running aggregation on the whole dataset, so we are not defining any column name in the function but aggregate function will only take numerical columns for work. So we will see outputs for all the numerical columns present in the dataset.

Example 1:

Input
 import pandas as pd
 df.aggregate(['sum', 'max']) Output

Id Total_marks Ranking
sum 279 14473 47
max 23 700 3

Let's apply different aggregation functions across different columns.

Example 1:

Input
 import pandas as pd

 df.aggregate({"Id":['sum', 'min'],
    "Total_marks":['max', 'min'],
    "Ranking":['min', 'sum']})
Output

Id Total_marks Ranking
sum 279 NaN 47
max NaN 700 NaN
min 1 550 1

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