cereniyim

# set the plot with different scale parameter and title
sns.violinplot(x="continent", y="life_expectancy", data=df,
               palette="Set3",
               order=["Africa", "Asia", "Americas", "Europe", "Oceania"],
               inner=None,
               scale="count")
plt.title("Violinplot of Life Expectancy Among Continents Between 1952 and 2007")

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def outlier_function(df, col_name):
    ''' this function detects first and third quartile and interquartile range for a given column of a dataframe
    then calculates upper and lower limits to determine outliers conservatively
    returns the number of lower and uper limit and number of outliers respectively
    '''
    first_quartile = np.percentile(np.array(df[col_name].tolist()), 25)
    third_quartile = np.percentile(np.array(df[col_name].tolist()), 75)
    IQR = third_quartile - first_quartile
                      
    upper_limit = third_quartile+(3*IQR)

cereniyim

# set the plot size
figsize(14,10)

# set the histogram, mean and median
sns.distplot(trees["Cover_Type"], kde=False)
plt.axvline(x=trees.Cover_Type.mean(), linewidth=3, color='g', label="mean", alpha=0.5)
plt.axvline(x=trees.Cover_Type.median(), linewidth=3, color='y', label="median", alpha=0.5)

# set title, legends and labels
plt.xlabel("Cover_Type")

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# distribution of the cover type in different wilderness areas
figsize(14,10)

# plot cover_type distribution for each wilderness area
for area in wilderness_areas:
    subset = trees[trees['Wilderness_Area_Type'] == area]
    sns.kdeplot(subset["Cover_Type"], label=area, linewidth=2)

# set title, legends and labels
plt.ylabel("Density")

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def split_numbers_chars(row):
    '''This function fetches the numerical characters at the end of a string
    and returns alphabetical character and numerical chaarcters respectively'''
    head = row.rstrip('0123456789')
    tail = row[len(head):]
    return head, tail

def reverse_one_hot_encode(dataframe, start_loc, end_loc, numeric_column_name):
    ''' this function takes the start and end location of the one-hot-encoded column set and numeric column name to be created as arguments
    1) transforms one-hot-encoded columns into one column consisting of column names with string data type

cereniyim

def corr_func(x, y, **kwargs):
    r = np.corrcoef(x, y)[0][1]
    ax = plt.gca()
    ax.annotate("r = {:.2f}".format(r),
                xy=(.2, .8), xycoords=ax.transAxes,
                size = 20)
                

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figsize=(16,14)

# plot the heatmap
colormap = plt.cm.RdBu
sns.heatmap(correlations,linewidths=0.1, 
            square=False, cmap=colormap, linecolor='white', annot=True)
plt.title('Pearson Correlation of Features with Numeric Soil_Type', size=14)

cereniyim

px.scatter(gapminder_df,
           x="Income",
           y="Life Expectancy",
           size="Population",
           size_max=60,
           color="Region",
           hover_name="Country",
           animation_frame="Year",
           animation_group="Country",
           color_discrete_sequence=["#FF69B4", "#87CEFA",

cereniyim

fig = px.scatter(gapminder_2018,
                 x="Income",
                 y="Life Expectancy",
                 size="Population",
                 size_max=60,
                 color="Region",
                 hover_name="Country",
                 facet_col="Income Level",
                 color_discrete_sequence=["#FF69B4", "#87CEFA",
                                          "#FFFF00", "#32CD32"],

cereniyim

import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge

#verilerin yüklenmesi
boston = load_boston()
X = boston.data
y = boston.target