วันที่ 25 — Pandas

pip install conda
conda install pandas

pip install conda
pip install pandas

import pandas as pd # importing pandas as pd
import numpy  as np # importing numpy as np

nums = [1, 2, 3, 4,5]
s = pd.Series(nums)
print(s)

    0    1
    1    2
    2    3
    3    4
    4    5
    dtype: int64

nums = [1, 2, 3, 4, 5]
s = pd.Series(nums, index=[1, 2, 3, 4, 5])
print(s)

    1    1
    2    2
    3    3
    4    4
    5    5
    dtype: int64

fruits = ['Orange','Banana','Mango']
fruits = pd.Series(fruits, index=[1, 2, 3])
print(fruits)

    1    Orange
    2    Banana
    3    Mango
    dtype: object

dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}

s = pd.Series(dct)
print(s)

    name       Asabeneh
    country     Finland
    city       Helsinki
    dtype: object

s = pd.Series(10, index = [1, 2, 3])
print(s)

    1    10
    2    10
    3    10
    dtype: int64

s = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)
print(s)

    0     5.000000
    1     6.666667
    2     8.333333
    3    10.000000
    4    11.666667
    5    13.333333
    6    15.000000
    7    16.666667
    8    18.333333
    9    20.000000
    dtype: float64

data = [
    ['Asabeneh', 'Finland', 'Helsink'],
    ['David', 'UK', 'London'],
    ['John', 'Sweden', 'Stockholm']
]
df = pd.DataFrame(data, columns=['Names','Country','City'])
print(df)

data = {'Name': ['Asabeneh', 'David', 'John'], 'Country':[
    'Finland', 'UK', 'Sweden'], 'City': ['Helsiki', 'London', 'Stockholm']}
df = pd.DataFrame(data)
print(df)

data = [
    {'Name': 'Asabeneh', 'Country': 'Finland', 'City': 'Helsinki'},
    {'Name': 'David', 'Country': 'UK', 'City': 'London'},
    {'Name': 'John', 'Country': 'Sweden', 'City': 'Stockholm'}]
df = pd.DataFrame(data)
print(df)

curl -O https://raw.githubusercontent.com/Asabeneh/30-Days-Of-Python/master/data/weight-height.csv

import pandas as pd

df = pd.read_csv('weight-height.csv')
print(df)

print(df.head()) # give five rows we can increase the number of rows by passing argument to the head() method

print(df.tail()) # tails give the last five rows, we can increase the rows by passing argument to tail method

print(df.shape) # as you can see 10000 rows and three columns

    (10000, 3)

print(df.columns)

    Index(['Gender', 'Height', 'Weight'], dtype='object')

heights = df['Height'] # this is now a series

print(heights)

    0       73.847017
    1       68.781904
    2       74.110105
    3       71.730978
    4       69.881796
              ...
    9995    66.172652
    9996    67.067155
    9997    63.867992
    9998    69.034243
    9999    61.944246
    Name: Height, Length: 10000, dtype: float64

weights = df['Weight'] # this is now a series

print(weights)

    0       241.893563
    1       162.310473
    2       212.740856
    3       220.042470
    4       206.349801
               ...
    9995    136.777454
    9996    170.867906
    9997    128.475319
    9998    163.852461
    9999    113.649103
    Name: Weight, Length: 10000, dtype: float64

print(len(heights) == len(weights))

    True

print(heights.describe()) # give statistical information about height data

    count    10000.000000
    mean        66.367560
    std          3.847528
    min         54.263133
    25%         63.505620
    50%         66.318070
    75%         69.174262
    max         78.998742
    Name: Height, dtype: float64

print(weights.describe())

    count    10000.000000
    mean       161.440357
    std         32.108439
    min         64.700127
    25%        135.818051
    50%        161.212928
    75%        187.169525
    max        269.989699
    Name: Weight, dtype: float64

print(df.describe())  # describe can also give statistical information from a dataFrame

import pandas as pd
import numpy as np
data = [
    {"Name": "Asabeneh", "Country":"Finland","City":"Helsinki"},
    {"Name": "David", "Country":"UK","City":"London"},
    {"Name": "John", "Country":"Sweden","City":"Stockholm"}]
df = pd.DataFrame(data)
print(df)

weights = [74, 78, 69]
df['Weight'] = weights
df

heights = [173, 175, 169]
df['Height'] = heights
print(df)

df['Height'] = df['Height'] * 0.01
df

# Using functions makes our code clean, but you can calculate the bmi without one
def calculate_bmi ():
    weights = df['Weight']
    heights = df['Height']
    bmi = []
    for w,h in zip(weights, heights):
        b = w/(h*h)
        bmi.append(b)
    return bmi

bmi = calculate_bmi()

df['BMI'] = bmi
df

df['BMI'] = round(df['BMI'], 1)
print(df)

birth_year = ['1769', '1985', '1990']
current_year = pd.Series(2020, index=[0, 1,2])
df['Birth Year'] = birth_year
df['Current Year'] = current_year
df

print(df.Weight.dtype)

    dtype('int64')

df['Birth Year'].dtype # it gives string object , we should change this to number

df['Birth Year'] = df['Birth Year'].astype('int')
print(df['Birth Year'].dtype) # let's check the data type now

    dtype('int32')

df['Current Year'] = df['Current Year'].astype('int')
df['Current Year'].dtype

    dtype('int32')

ages = df['Current Year'] - df['Birth Year']
ages

    0    251
    1     35
    2     30
    dtype: int32

df['Ages'] = ages
print(df)

mean = (35 + 30)/ 2
print('Mean: ',mean)	#it is good to add some description to the output, so we know what is what

   Mean:  32.5

print(df[df['Ages'] > 120])

print(df[df['Ages'] < 120])