Uvod u Python Pandas¶

Četvrti deo¶

Pandas metode Mean, Median i Mode¶

Mean - prosečna vrednost
Median - srednja vrednost
Mode - najčešća vrednost

Pandas DataFrame `mean()` metod¶

Metod mean()vraća Series sa prosečnom vrednošću svake kolone

# Odredi prosečnu (srednju) vrednost za svaku kolonu

import pandas as pd

data = [[1, 1, 2], [6, 4, 2], [4, 2, 1], [4, 2, 3]]

df = pd.DataFrame(data)
df

print(df.mean())

0    3.75
1    2.25
2    2.00
dtype: float64

Sintaksa¶

dataframe.mean(axis, skipna, level, numeric_only, kwargs)

Parametri:

axis: {index (0), columns (1)}
Opciono, osa na koju treba primeniti funkciju. Podrazumevana vrednost je 0.

skipna: bool, podrazumevano True

Isključuje NA/null vrednosti prilikom izračunavanja rezultata.
level: int ili ime nivoa

Opciono, podrazumevano None. Određuje koji nivo (u hijerarhijskom više indeksu) da proverite
numeric_only: bool

Opciono. Odredite da li želite da proverite samo numeričke vrednosti. Podrazumevano None
**kwargs

Opcioni, argumenti ključne reči. Ovi argumenti nemaju efekta, ali ih može prihvatiti funkcija NumPi

Metoda vraća: Series ili DataFrame ako je naveden nivo

import pandas as pd
  

df = pd.DataFrame({"A":[12, 4, 5, None, 1],
                   "B":[7, 2, 54, 3, None],
                   "C":[20, 16, 11, 3, 8],
                   "D":[14, 3, None, 2, 6]})
  

df.mean(axis = 0)

A     5.50
B    16.50
C    11.60
D     6.25
dtype: float64

import pandas as pd
  

df = pd.DataFrame({"A":[12, 4, 5, None, 1],
                   "B":[7, 2, 54, 3, None],
                   "C":[20, 16, 11, 3, 8],
                   "D":[14, 3, None, 2, 6]})
  

df.mean(axis = 1, skipna = True)

0    13.250000
1     6.250000
2    23.333333
3     2.666667
4     5.000000
dtype: float64

Vežbe¶

Za sledeće vežbe, koristite skup podataka IMDB-Movie-Data.csv sa skupom naslova kao indeksom (Title).
Dobra praksa je da skratite svoj izlaz metodom head().

Vežba 1¶

Izračunati prosečnu vrednost za kolonu Rating

import pandas as pd
dfFilm = pd.read_csv('../datasets/IMDB-Movie-Data.csv',index_col='Title')
dfFilm.head()

dfFilm['Rating'].mean()

6.723200000000003

Pandas DataFrame `median()` metod¶

Metod median()vraća Series sa srednjom vrednošću svake kolone

import pandas as pd

speed = [99,86,87,88,111,86,103,87,94,78,77,85,86]
spead = speed.sort()
speed

[77, 78, 85, 86, 86, 86, 87, 87, 88, 94, 99, 103, 111]

77, 78, 85, 86, 86, 86, 87, 87, 88, 94, 99, 103, 111

df = pd.DataFrame(speed)
df.median()

0    87.0
dtype: float64

Ako su u sredini dva broja median će biti aritmetička sredina tih brojeva

77, 78, 85, 86, 86, 86, 87 88, 94, 99, 103, 111

import pandas as pd

speed1 = [99,86,87,88,111,86,103,94,78,77,85,86]

df = pd.DataFrame(speed1)
df.median()

0    86.5
dtype: float64

# Odredi srednju vrednost za svaku kolonu
import pandas as pd

data = [[1, 1, 2], [6, 4, 2], [4, 2, 1], [4, 2, 3]]

df = pd.DataFrame(data)
df

print(df.median())

0    4.0
1    2.0
2    2.0
dtype: float64

import pandas as pd
  
# Kreiranje dataframe-a 
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
                   "B":[7, 2, 54, 3, None],
                   "C":[20, 16, 11, 3, 8],
                   "D":[14, 3, None, 2, 6]})
  

df.median(axis = 1)

# Obrazložiti izlaz

0    13.0
1     3.5
2    11.0
3     3.0
4     6.0
dtype: float64

Vežba 2¶

Izračunati srednju vrednost za kolonu Rating.

dfFilm['Rating'].median()

6.8

Pandas DataFrame `mode()` metod¶

Metod mode()vraća DataFrame sa vrednostima koje se najviše puta pojavljuju u određenoj koloni

speed1 = [99,86,87,88,111,86,103,94,78,77,85,86]

df = pd.DataFrame(speed)
df.mode()

import pandas as pd

data = [[1, 1, 2], [6, 4, 2], [4, 2, 1], [4, 2, 3]]

df = pd.DataFrame(data)
print (df)
df.mode()

   0  1  2
0  1  1  2
1  6  4  2
2  4  2  1
3  4  2  3

Vežba 3¶

Odrediti najčešću vrednost za kolone Rating i Year.

dfFilm[['Rating']].mode()

dfFilm['Year'].mode()

0    2016
dtype: int64

Vežba 4¶

Učitati skup podataka weather.csv
Izračunati prosečnu temperaturu, srednju vrednost za kolonu temperature
Za period od 2018-11-09 do 2018-11-16 izračunati prosečnu temperaturu, a zatim prikazati linijskim dijagramom temperature po danima za navedeni period.

import pandas as pd
df = pd.read_csv('../datasets/weather.csv')
#df.loc[:,'temperature'].mean()
df['temperature'].mean()

72.47816382880836

df['temperature'].median()

75.0

df['temperature'].mode()

0    88.0
dtype: float64

# prosečna temperatura za period od 2018-11-09 do 2018-11-16

import pandas as pd
df = pd.read_csv('../datasets/weather.csv', index_col=0)
df.loc ['2018-11-09':'2018-11-16', 'temperature']. mean()

50.037499999999994

import matplotlib.pyplot as plt

y = df.loc ['2018-11-09':'2018-11-16', 'temperature']
y

date
2018-11-09    53.1
2018-11-10    52.0
2018-11-11    51.1
2018-11-12    48.0
2018-11-13    51.1
2018-11-14    48.0
2018-11-15    43.0
2018-11-16    54.0
Name: temperature, dtype: float64

plt.plot(y.index,y)
plt.show()

	Rank	Genre	Description	Director	Actors	Year	Runtime (Minutes)	Rating	Votes	Revenue (Millions)	Metascore
Title
Guardians of the Galaxy	1	Action,Adventure,Sci-Fi	A group of intergalactic criminals are forced ...	James Gunn	Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...	2014	121	8.1	757074	333.13	76.0
Prometheus	2	Adventure,Mystery,Sci-Fi	Following clues to the origin of mankind, a te...	Ridley Scott	Noomi Rapace, Logan Marshall-Green, Michael Fa...	2012	124	7.0	485820	126.46	65.0
Split	3	Horror,Thriller	Three girls are kidnapped by a man with a diag...	M. Night Shyamalan	James McAvoy, Anya Taylor-Joy, Haley Lu Richar...	2016	117	7.3	157606	138.12	62.0
Sing	4	Animation,Comedy,Family	In a city of humanoid animals, a hustling thea...	Christophe Lourdelet	Matthew McConaughey,Reese Witherspoon, Seth Ma...	2016	108	7.2	60545	270.32	59.0
Suicide Squad	5	Action,Adventure,Fantasy	A secret government agency recruits some of th...	David Ayer	Will Smith, Jared Leto, Margot Robbie, Viola D...	2016	123	6.2	393727	325.02	40.0