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8-data_analysis_with_python-matplotlib-seaborn.py
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8-data_analysis_with_python-matplotlib-seaborn.py
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###############################################
# PYTHON İLE VERİ ANALİZİ (DATA ANALYSIS WITH PYTHON)
###############################################
# - Veri Görselleştirme: Matplotlib & Seaborn
#############################################
# VERİ GÖRSELLEŞTİRME: MATPLOTLIB & SEABORN
#############################################
################################
# MATPLOTLIB
################################
# Kategorik değişken: sütun grafik --> "countplot" ve "bar" ile yapılabiliyor
# Sayısal değişken: hist, boxplot
# kısa bir not: veritabanı ile ilişkili işlerde python matplotlib vs den ziyade powerbi gibi
# direkt veritabanı ile ilişkili araçları kullanmak daha elverişlidir
#############################################
# Kategorik Değişken Görselleştirme
#############################################
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 500)
df = sns.load_dataset("titanic")
df.head()
df['sex'].value_counts().plot(kind='bar')
# kategorik değişkenler ile işlem yapmak istediğimizde aklımıza ilk gelmesi
# gereken fonksiyonlardan birisi value_counts() fonksiyonudur
plt.show()
#############################################
# Sayısal Değişken Görselleştirme
#############################################
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 500)
df = sns.load_dataset("titanic")
df.head()
plt.hist(df["age"])
plt.show()
plt.boxplot(df["fare"])
plt.show()
# hist ve boxplot istatiksel iki grafiktir.
#############################################
# Matplotlib'in Özellikleri
#############################################
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 500)
#######################
# plot
#######################
x = np.array([1, 8])
y = np.array([0, 150])
plt.plot(x, y)
plt.show()
plt.plot(x, y, 'o')
plt.show()
x = np.array([2, 4, 6, 8, 10])
y = np.array([1, 3, 5, 7, 9])
plt.plot(x, y)
plt.show()
plt.plot(x, y, 'o')
plt.show()
#######################
# marker
#######################
y = np.array([13, 28, 11, 100])
plt.plot(y, marker='o')
plt.show()
plt.plot(y, marker='*')
plt.show()
markers = ['o', '*', '.', ',', 'x', 'X', '+', 'P', 's', 'D', 'd', 'p', 'H', 'h']
#######################
# line
#######################
y = np.array([13, 28, 11, 100])
plt.plot(y, linestyle="dashdot", color="r")
plt.show()
#######################
# Multiple Lines
#######################
x = np.array([23, 18, 31, 10])
y = np.array([13, 28, 11, 100])
plt.plot(x)
plt.plot(y)
plt.show()
#######################
# Labels
#######################
x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])
plt.plot(x, y)
# Başlık
plt.title("Bu ana başlık")
# X eksenini isimlendirme
plt.xlabel("X ekseni isimlendirmesi")
plt.ylabel("Y ekseni isimlendirmesi")
plt.grid()
plt.show()
#######################
# Subplots
#######################
# plot 1
x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])
plt.subplot(1, 2, 1)
plt.title("1")
plt.plot(x, y)
# plot 2
x = np.array([8, 8, 9, 9, 10, 15, 11, 15, 12, 15])
y = np.array([24, 20, 26, 27, 280, 29, 30, 30, 30, 30])
plt.subplot(1, 2, 2)
plt.title("2")
plt.plot(x, y)
plt.show()
# 3 grafiği bir satır 3 sütun olarak konumlamak.
# plot 1
x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])
plt.subplot(1, 3, 1)
plt.title("1")
plt.plot(x, y)
# plot 2
x = np.array([8, 8, 9, 9, 10, 15, 11, 15, 12, 15])
y = np.array([24, 20, 26, 27, 280, 29, 30, 30, 30, 30])
plt.subplot(1, 3, 2)
plt.title("2")
plt.plot(x, y)
# plot 3
x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])
plt.subplot(1, 3, 3)
plt.title("3")
plt.plot(x, y)
plt.show()
################################
# SEABORN
################################
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
df = sns.load_dataset("tips")
df.head()
df["sex"].value_counts()
sns.countplot(x=df["sex"], data=df) # kategorik görselleştirme
plt.show()
# bu alt kısım ise matplotlib de oluşturma biçimi;
df['sex'].value_counts().plot(kind='bar')
plt.show()
#############################################
# Sayısal Değişken Görselleştirme
#############################################
sns.boxplot(x=df["total_bill"])
plt.show()
df["total_bill"].hist()
plt.show()