Selecting top and bottom records - Read from TXT file - Export to TXT file  - Descriptive statistics with Python Pandas Module

 

In [1]:

import pandas as pd
from numpy import random
import matplotlib.pyplot as plt

%matplotlib inline

 

In [2]:

names = ['Bob','Jessica','Mary','John','Mel']

 

In [3]:

random.seed(500)
random_names = [names[random.randint(low=0,high=len(names))] for i in range(1000)]
random_names[:5]

Out[3]:

['Mary', 'Jessica', 'Jessica', 'Bob', 'Jessica']

 

In [4]:

births = [random.randint(low=1, high=1000) for i in range(1000)]
births[:5]

Out[4]:

[969, 156, 78, 579, 974]

 

Create list by marging two list:

In [5]:

BabyData = list(zip(random_names, births))
BabyData[:5]

Out[5]:

[('Mary', 969),
 ('Jessica', 156),
 ('Jessica', 78),
 ('Bob', 579),
 ('Jessica', 974)]

 

Creating DataFrame:

In [6]:

df = pd.DataFrame(data=BabyData, columns=['Names', 'Births'])

 

In [7]:

df[:5]

Out[7]:

	Names	Births
0	Mary	969
1	Jessica	156
2	Jessica	78
3	Bob	579
4	Jessica	974

 

Write to CSV file:

In [8]:

df.to_csv('Data/births1880.csv', index=False, header=False)

 

In [9]:

df = pd.read_csv('Data/births1880.csv')
df.info()

Out[9]:

RangeIndex: 999 entries, 0 to 998
Data columns (total 2 columns):
Mary    999 non-null object
969     999 non-null int64
dtypes: int64(1), object(1)
memory usage: 15.7+ KB

 

In [10]:

df.head()

Out[10]:

	Mary	969
0	Jessica	156
1	Jessica	78
2	Bob	579
3	Jessica	974
4	Jessica	125

 

In [11]:

df = pd.read_csv('Data/births1880.csv', header=None)
df.info()

Out[11]:

RangeIndex: 1000 entries, 0 to 999
Data columns (total 2 columns):
0    1000 non-null object
1    1000 non-null int64
dtypes: int64(1), object(1)
memory usage: 15.7+ KB

 

In [12]:

df.tail()

Out[12]:

	0	1
995	John	152
996	Jessica	512
997	John	757
998	Jessica	295
999	John	153

 

In [13]:

df = pd.read_csv('Data/births1880.csv', names=['Names','Births'])
df.head(5)

Out[13]:

	Names	Births
0	Mary	969
1	Jessica	156
2	Jessica	78
3	Bob	579
4	Jessica	974

 

In [14]:

df['Names'].unique()

Out[14]:

array(['Mary', 'Jessica', 'Bob', 'John', 'Mel'], dtype=object)

 

In [15]:

for x in df['Names'].unique():
    print(x)

Out[15]:

Mary
Jessica
Bob
John
Mel

 

In [16]:

print(df['Names'].describe())

Out[16]:

count     1000
unique       5
top        Bob
freq       206
Name: Names, dtype: object

 

Group By:

In [17]:

name = df.groupby('Names')
df = name.sum()

df

Out[18]:

	Births
Names
Bob	107023
Jessica	98024
John	90899
Mary	99636
Mel	102523

 

Analyze Data:

Sorting Data:

In [19]:

Sorted = df.sort_values(['Births'], ascending=False)
Sorted.head(1)

Out[19]:

	Births
Names
Bob	107023

 

In [20]:

df['Births'].max()

Out[20]:

107023

 

Present Data:

In [21]:

df['Births'].plot.bar()
print("The most popular name")
df.sort_values(by='Births', ascending=False)

Out[21]:

	Births
Names
Bob	107023
Mel	102523
Mary	99636
Jessica	98024
John	90899