path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
16136181/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.info() | code |
16136181/cell_29 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data.month.unique()
cat = ['admin_pages', 'info_pages', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend']
cont = ['admin_duration', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value']
mask = np.array(data1[cont].corr())
mask[np.tril_indices_from(data1[cont].corr())] = False
def cat_data(i):
pass
for i in cat:
cat_data(i)
from scipy.stats import skew
sns.set()
def continous_data(i):
pass
for i in cont:
continous_data(i)
def cat_bivar(i):
sns.barplot(data[i], data1.revenue)
print('--' * 60)
plt.title('Bar-plot of Revenue against ' + str(i))
plt.show()
for i in cat:
cat_bivar(i) | code |
16136181/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
cat = ['admin_pages', 'info_pages', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend']
cont = ['admin_duration', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value']
mask = np.array(data1[cont].corr())
mask[np.tril_indices_from(data1[cont].corr())] = False
for i in cont:
Q1 = data1[i].quantile(0.25)
Q3 = data1[i].quantile(0.75)
IQR = Q3 - Q1
upper = Q3 + 1.5 * IQR
lower = Q1 - 1.5 * IQR
outlier_count = data1[i][(data1[i] < lower) | (data1[i] > upper)].count()
total = data1[i].count()
percent = outlier_count / total * 100
print('Percentage of Outliers in {} column :: {}%'.format(i, np.round(percent, 2))) | code |
16136181/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data.month.unique() | code |
16136181/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
print(os.listdir('../input')) | code |
16136181/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
print('Descriptive statistics of Data')
data.describe().T | code |
16136181/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data1.head(10) | code |
16136181/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data1.info() | code |
16136181/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape | code |
16136181/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
pd.isnull(data1).sum() | code |
16136181/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/online_shoppers_intention.csv')
data.shape
data.describe().T
data.columns = ['admin_pages', 'admin_duration', 'info_pages', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend', 'revenue']
data1 = data.copy()
data1.weekend = np.where(data.weekend == True, 1, 0)
data1.revenue = np.where(data.revenue == True, 1, 0)
data.month.unique()
cat = ['admin_pages', 'info_pages', 'spl_day', 'month', 'os', 'browser', 'region', 'traffic_type', 'visitor_type', 'weekend']
cont = ['admin_duration', 'info_duration', 'product_pages', 'prod_duration', 'avg_bounce_rate', 'avg_exit_rate', 'avg_page_value']
mask = np.array(data1[cont].corr())
mask[np.tril_indices_from(data1[cont].corr())] = False
def cat_data(i):
sns.countplot(data[i])
print('--' * 60)
plt.title('Count plot of ' + str(i))
plt.show()
for i in cat:
cat_data(i) | code |
49126907/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from kaggle_environments import make
from kaggle_environments.envs.rps.agents import agents
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
from kaggle_environments import make
from kaggle_environments.envs.rps.agents import agents
import numpy as np
import pandas as pd
from tqdm.auto import tqdm
all_agents = ['submission.py', 'submission1.py', 'submission2.py', 'submission3.py', 'nash.py', 'nash2.py', 'nash3.py'] + list(agents.values())
all_agent_names = ['submit', 'submit1', 'submit2', 'submit3', 'nash', 'nash2', 'nash3'] + list((k[:8] for k in agents.keys()))
env = make('rps', configuration={'episodeSteps': 1000}, debug=True)
L = len(all_agents)
results = np.zeros((L, L), dtype=int)
N = 100
for it in tqdm(range(N), total=N):
for a1 in range(L):
a2 = 0
env.run([all_agents[a1], all_agents[a2]])
rewards = env.toJSON()['rewards']
results[a1, a2] += -1 if rewards[0] is None else int(rewards[0] > 0) - int(rewards[0] < 0)
results[a2, a1] += -1 if rewards[1] is None else int(rewards[1] > 0) - int(rewards[1] < 0)
print(pd.DataFrame(results, columns=all_agent_names, index=all_agent_names)) | code |
49126907/cell_4 | [
"text_plain_output_1.png"
] | import secrets
Jmin = 0
Jmax = 5
J = Jmin + secrets.randbelow(Jmax - Jmin + 1)
Dmin = 2
Dmax = 5
Hash = []
Map = []
MyMap = []
for D in range(Dmin, Dmax + 1):
Hash.append([0, 0, 0])
Map.append([{}, {}, {}])
MyMap.append([{}, {}, {}])
G = 2
R = 0.4
V = 0.7
VM = 0.7
B = 0
def add(map1, hash1, A):
if hash1 not in map1:
map1[hash1] = {'S': 0}
d = map1[hash1]
if A not in d:
d[A] = 1
else:
d[A] += 1
d['S'] += 1
def match(map1, hash1, S):
global B
global J
if hash1 not in map1:
return
d = map1[hash1]
if d['S'] >= G:
for A in range(S):
if A in d and d[A] >= d['S'] * R + (1 - R) * G and (secrets.randbelow(101) < 100 * V):
if secrets.randbelow(101) < 100 * VM:
B = (A + 1) % S
else:
B = A % S
J = Jmin + secrets.randbelow(Jmax - Jmin + 1)
def agent(observation, configuration):
global B
global J
T = observation.step
S = configuration.signs
if T > 0:
A = observation.lastOpponentAction
BA = (B + 1) % S
B = secrets.randbelow(S)
for D in range(Dmin, Dmax + 1):
if T > D:
add(Map[D - Dmin][0], Hash[D - Dmin][0], A)
add(Map[D - Dmin][1], Hash[D - Dmin][1], A)
add(Map[D - Dmin][2], Hash[D - Dmin][2], A)
add(MyMap[D - Dmin][0], Hash[D - Dmin][0], BA)
add(MyMap[D - Dmin][1], Hash[D - Dmin][1], BA)
add(MyMap[D - Dmin][2], Hash[D - Dmin][2], BA)
if T > 0:
Hash[D - Dmin][0] = Hash[D - Dmin][0] // S ** 2 + (A + S * B) * S ** (2 * D - 1)
Hash[D - Dmin][1] = Hash[D - Dmin][1] // S + A * S ** (D - 1)
Hash[D - Dmin][2] = Hash[D - Dmin][2] // S + B * S ** (D - 1)
if J == 0:
match(Map[D - Dmin][0], Hash[D - Dmin][0], S)
match(Map[D - Dmin][1], Hash[D - Dmin][1], S)
match(Map[D - Dmin][2], Hash[D - Dmin][2], S)
if J == 0:
match(MyMap[D - Dmin][0], Hash[D - Dmin][0], S)
match(MyMap[D - Dmin][1], Hash[D - Dmin][1], S)
match(MyMap[D - Dmin][2], Hash[D - Dmin][2], S)
if J > 0:
J -= 1
return B | code |
49126907/cell_6 | [
"text_plain_output_1.png"
] | import random
rng = random.SystemRandom()
def agent(observation, configuration):
S = configuration.signs
return rng.randrange(0, S) | code |
49126907/cell_2 | [
"text_plain_output_1.png"
] | import random
rng = random.SystemRandom()
hash1 = 0
hash2 = 0
hash3 = 0
map1 = {}
map2 = {}
map3 = {}
Jmin = 10
Jmax = 20
J = rng.randrange(Jmin, Jmax + 1)
D = 2
G = 3
R = 0.6
B = 0
def add(map1, hash1, A):
if hash1 not in map1:
map1[hash1] = {'S': 0}
d = map1[hash1]
if A not in d:
d[A] = 1
else:
d[A] += 1
d['S'] += 1
def match(map1, hash1, S):
global B
global J
if hash1 not in map1:
return
d = map1[hash1]
if d['S'] >= G:
for A in range(S):
if A in d and d[A] >= d['S'] * R:
B = (A + 1) % S
J = rng.randrange(Jmin, Jmax)
def agent(observation, configuration):
global B
global J
global hash1
global hash2
global hash3
T = observation.step
S = configuration.signs
if T > 0:
A = observation.lastOpponentAction
if T > D:
add(map1, hash1, A)
add(map2, hash2, A)
add(map3, hash3, A)
if T > 0:
hash1 = hash1 // S + A * S ** (D - 1)
hash2 = hash2 // S + B * S ** (D - 1)
hash3 = hash3 // S ** 2 + (A + S * B) * S ** (2 * D - 1)
B = rng.randrange(0, S)
if J == 0:
match(map1, hash1, S)
match(map2, hash2, S)
match(map3, hash3, S)
else:
J -= 1
return B | code |
49126907/cell_1 | [
"text_plain_output_1.png"
] | import secrets
import math
Jmax = 2
J = Jmax - int(math.sqrt(secrets.randbelow((Jmax + 1) ** 2)))
Dmin = 2
Dmax = 5
Hash = []
Map = []
MyMap = []
for D in range(Dmin, Dmax + 1):
Hash.append([0, 0, 0])
Map.append([{}, {}, {}])
MyMap.append([{}, {}, {}])
G = 2
R = 0.4
V = 0.8
VM = 0.95
B = 0
DT = 200
def add(map1, hash1, A, T):
if hash1 not in map1:
map1[hash1] = {'S': []}
d = map1[hash1]
if A not in d:
d[A] = [T]
else:
d[A].append(T)
d['S'].append(T)
def rank(A, T):
return len([a for a in A if a > T - DT])
def match(map1, hash1, S, T):
global B
global J
if hash1 not in map1:
return
d = map1[hash1]
if rank(d['S'], T) >= G:
for A in range(S):
if A in d and rank(d[A], T) >= rank(d['S'], T) * R + (1 - R) * G and (secrets.randbelow(1001) < 1000 * V):
if secrets.randbelow(1001) < 1000 * VM:
B = (A + 1) % S
else:
B = A % S
J = Jmax - int(math.sqrt(secrets.randbelow((Jmax + 1) ** 2)))
def agent(observation, configuration):
global B
global J
T = observation.step
S = configuration.signs
if T > 0:
A = observation.lastOpponentAction
BA = (B + 1) % S
B = secrets.randbelow(S)
for D in range(Dmin, Dmax + 1):
if T > D:
add(Map[D - Dmin][0], Hash[D - Dmin][0], A, T)
add(Map[D - Dmin][1], Hash[D - Dmin][1], A, T)
add(Map[D - Dmin][2], Hash[D - Dmin][2], A, T)
add(MyMap[D - Dmin][0], Hash[D - Dmin][0], BA, T)
add(MyMap[D - Dmin][1], Hash[D - Dmin][1], BA, T)
add(MyMap[D - Dmin][2], Hash[D - Dmin][2], BA, T)
if T > 0:
Hash[D - Dmin][0] = Hash[D - Dmin][0] // S ** 2 + (A + S * B) * S ** (2 * D - 1)
Hash[D - Dmin][1] = Hash[D - Dmin][1] // S + A * S ** (D - 1)
Hash[D - Dmin][2] = Hash[D - Dmin][2] // S + B * S ** (D - 1)
if J == 0:
match(Map[D - Dmin][0], Hash[D - Dmin][0], S, T)
match(Map[D - Dmin][1], Hash[D - Dmin][1], S, T)
match(Map[D - Dmin][2], Hash[D - Dmin][2], S, T)
if J == 0:
match(MyMap[D - Dmin][0], Hash[D - Dmin][0], S, T)
match(MyMap[D - Dmin][1], Hash[D - Dmin][1], S, T)
match(MyMap[D - Dmin][2], Hash[D - Dmin][2], S, T)
if J > 0:
J -= 1
return B | code |
49126907/cell_7 | [
"text_plain_output_1.png"
] | import secrets
def agent(observation, configuration):
S = configuration.signs
return secrets.randbelow(S) | code |
49126907/cell_3 | [
"text_plain_output_1.png"
] | import secrets
hash1 = 0
hash2 = 0
hash3 = 0
map1 = {}
map2 = {}
map3 = {}
Jmin = 10
Jmax = 30
J = Jmin + secrets.randbelow(Jmax - Jmin + 1)
D = 3
G = 2
R = 0.7
B = 0
def add(map1, hash1, A):
if hash1 not in map1:
map1[hash1] = {'S': 0}
d = map1[hash1]
if A not in d:
d[A] = 1
else:
d[A] += 1
d['S'] += 1
def match(map1, hash1, S):
global B
global J
if hash1 not in map1:
return
d = map1[hash1]
if d['S'] >= G:
for A in range(S):
if A in d and d[A] >= d['S'] * R:
B = (A + 1) % S
J = Jmin + secrets.randbelow(Jmax - Jmin + 1)
def agent(observation, configuration):
global B
global J
global hash1
global hash2
global hash3
T = observation.step
S = configuration.signs
if T > 0:
A = observation.lastOpponentAction
if T > D:
add(map1, hash1, A)
add(map2, hash2, A)
add(map3, hash3, A)
if T > 0:
hash1 = hash1 // S + A * S ** (D - 1)
hash2 = hash2 // S + B * S ** (D - 1)
hash3 = hash3 // S ** 2 + (A + S * B) * S ** (2 * D - 1)
B = secrets.randbelow(S)
if J == 0:
match(map1, hash1, S)
match(map2, hash2, S)
match(map3, hash3, S)
else:
J -= 1
return B | code |
49126907/cell_5 | [
"text_plain_output_1.png"
] | import random
def agent(observation, configuration):
S = configuration.signs
return random.randrange(0, S) | code |
16117706/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb_data['TeamName'].isin(top_15_teams)]
top_15_subs = top_15_subs.drop('TeamId', axis=1)
top_15_subs.pivot(columns='TeamName', values='Score')
for i in top_15_subs.pivot(columns='TeamName', values='Score').interpolate():
top_15_subs.pivot(columns='TeamName', values='Score')[i].dropna().plot(legend=True, ylim=(0.93, 0.95), figsize=(12, 12), title=str(i))
top_15_subs.index = pd.to_datetime(top_15_subs.index)
top_15_subs_last_7 = top_15_subs.loc[top_15_subs.index > '2019-6-15']
for i in top_15_subs_last_7.pivot(columns='TeamName', values='Score').interpolate():
top_15_subs_last_7.pivot(columns='TeamName', values='Score')[i].dropna().plot(legend=True, ylim=(0.93, 0.95), figsize=(12, 12), title=str(i))
plt.show() | code |
16117706/cell_9 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb_data['TeamName'].isin(top_15_teams)]
top_15_subs = top_15_subs.drop('TeamId', axis=1)
top_15_subs.pivot(columns='TeamName', values='Score')
for i in top_15_subs.pivot(columns='TeamName', values='Score').interpolate():
print(top_15_subs.pivot(columns='TeamName', values='Score')[i].dropna()) | code |
16117706/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb_data['TeamName'].isin(top_15_teams)]
top_15_subs = top_15_subs.drop('TeamId', axis=1)
top_15_subs.pivot(columns='TeamName', values='Score') | code |
16117706/cell_8 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png"
] | import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb_data['TeamName'].isin(top_15_teams)]
top_15_subs = top_15_subs.drop('TeamId', axis=1)
top_15_subs.pivot(columns='TeamName', values='Score')
top_15_subs.pivot(columns='TeamName', values='Score').interpolate().plot(legend=True, ylim=(0.93, 0.95), figsize=(12, 12)) | code |
16117706/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
lb_data = pd.read_csv('../input/leaderboard-4-days-out/jigsaw-unintended-bias-in-toxicity-classification-publicleaderboard.csv')
lb_data = lb_data.set_index('SubmissionDate')
top_15_teams = lb_data.groupby('TeamId').max().sort_values('Score')[-15:]['TeamName'].values
top_15_subs = lb_data.loc[lb_data['TeamName'].isin(top_15_teams)]
top_15_subs = top_15_subs.drop('TeamId', axis=1)
top_15_subs.pivot(columns='TeamName', values='Score')
for i in top_15_subs.pivot(columns='TeamName', values='Score').interpolate():
top_15_subs.pivot(columns='TeamName', values='Score')[i].dropna().plot(legend=True, ylim=(0.93, 0.95), figsize=(12, 12), title=str(i))
plt.show() | code |
17110052/cell_13 | [
"text_html_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from subprocess import check_output
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
#try to set index to dataframe
fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank","Standard Error":"Standard_Error"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Dystopia Residual":"Dystopia_Residual","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
plt.legend(loc='upper right')
data_2015U=data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0,0],kind = 'line', color = 'red',title = 'Happiness Score',linewidth=1,grid = True,linestyle = ':')
data_2015U.Family.plot( ax=axes[0,1],kind='line' ,color='green' ,title='Family' ,linewidth=1 , grid=True ,linestyle=':' )
data_2015U.Economy.plot( ax=axes[1,0],kind='line' ,color='yellow', title='Economy',linewidth=1,grid=True ,linestyle=':' )
data_2015U.Health.plot( ax=axes[1,1],kind='line' ,color='blue', title='Health',linewidth=1,grid=True ,linestyle=':' )
# legend = puts label into plot
# label = name of label
# title = title of plot
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(50)
plt.colorbar()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input'))
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
data2015 = pd.read_csv('../input/2015.csv')
data_updated = data2015.rename(index=str, columns={'Happiness Rank': 'Happiness_Rank'})
data_2015U = data_updated.rename(index=str, columns={'Happiness Score': 'Happiness_Score'})
data_2015U = data_2015U.rename(index=str, columns={'Economy (GDP per Capita)': 'Economy', 'Health (Life Expectancy)': 'Health', 'Trust (Government Corruption)': 'Trust'})
f, ax = plt.subplots(figsize=(20, 12))
Western_Europe = data_2015U[data_2015U.Region == 'Western Europe']
North_America = data_2015U[data_2015U.Region == 'North America']
Australian_New_Zealand = data_2015U[data_2015U.Region == 'Australia and New Zealand']
Middle_East_and_Northern_Africa = data_2015U[data_2015U.Region == 'Middle East and Northern Africa']
Latin_America_and_Caribbean = data_2015U[data_2015U.Region == 'Latin America and Caribbean']
Southeastern_Asia = data_2015U[data_2015U.Region == 'Southeastern Asia']
Central_and_Eastern_Europe = data_2015U[data_2015U.Region == 'Central and Eastern Europe']
Eastern_Asia = data_2015U[data_2015U.Region == 'Eastern_Asia']
Southern_Asia = data_2015U[data_2015U.Region == 'Southern Asia']
for each in range(0, len(Western_Europe.Country)):
x = Western_Europe.Happiness_Score[each]
y = Western_Europe.Freedom[each]
plt.scatter(Western_Europe.Happiness_Score, Western_Europe.Freedom, color='red', linewidth=1)
plt.text(x, y, Western_Europe.Country[each], fontsize=12)
for each in range(0, len(North_America.Country)):
x = North_America.Happiness_Score[each]
y = North_America.Freedom[each]
plt.scatter(North_America.Happiness_Score, North_America.Freedom, color='blue', linewidth=1)
plt.text(x, y, North_America.Country[each], fontsize=12)
for each in range(0, len(Middle_East_and_Northern_Africa.Country)):
x = Middle_East_and_Northern_Africa.Happiness_Score[each]
y = Middle_East_and_Northern_Africa.Freedom[each]
plt.scatter(Middle_East_and_Northern_Africa.Happiness_Score, Middle_East_and_Northern_Africa.Freedom, color='purple', linewidth=1)
plt.text(x, y, Middle_East_and_Northern_Africa.Country[each], fontsize=12)
plt.title('Happiness Score-Freedom Scatter Plot')
plt.xlabel('Happiness Score')
plt.ylabel('Freedom') | code |
17110052/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
for each in data2015.columns:
print(each) | code |
17110052/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.head() | code |
17110052/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f, ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax)
plt.show() | code |
17110052/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
fig, axes = plt.subplots(figsize=(10, 10), nrows=2, ncols=2)
data_updated = data2015.rename(index=str, columns={'Happiness Rank': 'Happiness_Rank', 'Standard Error': 'Standard_Error'})
data_2015U = data_updated.rename(index=str, columns={'Happiness Score': 'Happiness_Score'})
data_2015U = data_2015U.rename(index=str, columns={'Economy (GDP per Capita)': 'Economy', 'Dystopia Residual': 'Dystopia_Residual', 'Health (Life Expectancy)': 'Health', 'Trust (Government Corruption)': 'Trust'})
plt.legend(loc='upper right')
data_2015U = data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0, 0], kind='line', color='red', title='Happiness Score', linewidth=1, grid=True, linestyle=':')
data_2015U.Family.plot(ax=axes[0, 1], kind='line', color='green', title='Family', linewidth=1, grid=True, linestyle=':')
data_2015U.Economy.plot(ax=axes[1, 0], kind='line', color='yellow', title='Economy', linewidth=1, grid=True, linestyle=':')
data_2015U.Health.plot(ax=axes[1, 1], kind='line', color='blue', title='Health', linewidth=1, grid=True, linestyle=':') | code |
17110052/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input'))
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17110052/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
data2015.head() | code |
17110052/cell_18 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from subprocess import check_output
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
#try to set index to dataframe
fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank","Standard Error":"Standard_Error"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Dystopia Residual":"Dystopia_Residual","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
plt.legend(loc='upper right')
data_2015U=data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0,0],kind = 'line', color = 'red',title = 'Happiness Score',linewidth=1,grid = True,linestyle = ':')
data_2015U.Family.plot( ax=axes[0,1],kind='line' ,color='green' ,title='Family' ,linewidth=1 , grid=True ,linestyle=':' )
data_2015U.Economy.plot( ax=axes[1,0],kind='line' ,color='yellow', title='Economy',linewidth=1,grid=True ,linestyle=':' )
data_2015U.Health.plot( ax=axes[1,1],kind='line' ,color='blue', title='Health',linewidth=1,grid=True ,linestyle=':' )
# legend = puts label into plot
# label = name of label
# title = title of plot
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(50)
plt.colorbar()
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory are saved as output.
data2015=pd.read_csv('../input/2015.csv')
#fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
f,ax = plt.subplots(figsize=(20, 12))
Western_Europe=data_2015U[ data_2015U.Region=='Western Europe']
North_America=data_2015U[ data_2015U.Region=='North America']
Australian_New_Zealand=data_2015U[ data_2015U.Region=='Australia and New Zealand']
Middle_East_and_Northern_Africa=data_2015U[ data_2015U.Region=='Middle East and Northern Africa']
Latin_America_and_Caribbean=data_2015U[ data_2015U.Region=='Latin America and Caribbean']
Southeastern_Asia=data_2015U[ data_2015U.Region=='Southeastern Asia']
Central_and_Eastern_Europe=data_2015U[ data_2015U.Region=='Central and Eastern Europe']
Eastern_Asia=data_2015U[ data_2015U.Region=='Eastern_Asia']
#Sub_Saharan_Africa=data_2015U[ data_2015U.Region=='Sub Saharan Africa']
Southern_Asia=data_2015U[ data_2015U.Region=='Southern Asia']
for each in range(0,len(Western_Europe.Country)):
x = Western_Europe.Happiness_Score[each]
y = Western_Europe.Freedom[each]
plt.scatter( Western_Europe.Happiness_Score,Western_Europe.Freedom,color='red',linewidth=1)
plt.text(x, y, Western_Europe.Country[each], fontsize=12)
for each in range(0,len(North_America.Country)):
x = North_America.Happiness_Score[each]
y = North_America.Freedom[each]
plt.scatter( North_America.Happiness_Score,North_America.Freedom,color='blue',linewidth=1)
plt.text(x, y, North_America.Country[each], fontsize=12)
for each in range(0,len( Middle_East_and_Northern_Africa.Country)):
x =Middle_East_and_Northern_Africa.Happiness_Score[each]
y =Middle_East_and_Northern_Africa.Freedom[each]
plt.scatter( Middle_East_and_Northern_Africa.Happiness_Score, Middle_East_and_Northern_Africa.Freedom,color='purple',linewidth=1)
plt.text(x, y, Middle_East_and_Northern_Africa.Country[each], fontsize=12)
plt.title("Happiness Score-Freedom Scatter Plot")
plt.xlabel("Happiness Score")
plt.ylabel("Freedom")
melted = pd.melt(frame=data_2015U, id_vars='Country', value_vars=['Generosity', 'Dystopia_Residual'])
melted.loc[:10]
data_2015U1=data_2015U.head()
data_2015U2=data_2015U.tail()
concat_data_row=pd.concat([data_2015U1,data_2015U2],axis=0,ignore_index=True)
concat_data_row
data1 = data_2015U.loc[:, ['Health', 'Trust', 'Freedom']]
fig, axes = plt.subplots(nrows=2, ncols=2)
data_2015U.plot(ax=axes[0, 0], kind='scatter', x='Happiness_Score', y='Freedom', color='blue')
data_2015U.plot(ax=axes[0, 1], kind='scatter', x='Happiness_Score', y='Family', color='red')
data_2015U.plot(ax=axes[1, 0], kind='scatter', x='Happiness_Score', y='Economy', color='yellow')
data_2015U.plot(ax=axes[1, 1], kind='scatter', x='Happiness_Score', y='Generosity', color='pink') | code |
17110052/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
data2015.tail() | code |
17110052/cell_15 | [
"image_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from subprocess import check_output
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
#try to set index to dataframe
fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank","Standard Error":"Standard_Error"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Dystopia Residual":"Dystopia_Residual","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
plt.legend(loc='upper right')
data_2015U=data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0,0],kind = 'line', color = 'red',title = 'Happiness Score',linewidth=1,grid = True,linestyle = ':')
data_2015U.Family.plot( ax=axes[0,1],kind='line' ,color='green' ,title='Family' ,linewidth=1 , grid=True ,linestyle=':' )
data_2015U.Economy.plot( ax=axes[1,0],kind='line' ,color='yellow', title='Economy',linewidth=1,grid=True ,linestyle=':' )
data_2015U.Health.plot( ax=axes[1,1],kind='line' ,color='blue', title='Health',linewidth=1,grid=True ,linestyle=':' )
# legend = puts label into plot
# label = name of label
# title = title of plot
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(50)
plt.colorbar()
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory are saved as output.
data2015=pd.read_csv('../input/2015.csv')
#fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
f,ax = plt.subplots(figsize=(20, 12))
Western_Europe=data_2015U[ data_2015U.Region=='Western Europe']
North_America=data_2015U[ data_2015U.Region=='North America']
Australian_New_Zealand=data_2015U[ data_2015U.Region=='Australia and New Zealand']
Middle_East_and_Northern_Africa=data_2015U[ data_2015U.Region=='Middle East and Northern Africa']
Latin_America_and_Caribbean=data_2015U[ data_2015U.Region=='Latin America and Caribbean']
Southeastern_Asia=data_2015U[ data_2015U.Region=='Southeastern Asia']
Central_and_Eastern_Europe=data_2015U[ data_2015U.Region=='Central and Eastern Europe']
Eastern_Asia=data_2015U[ data_2015U.Region=='Eastern_Asia']
#Sub_Saharan_Africa=data_2015U[ data_2015U.Region=='Sub Saharan Africa']
Southern_Asia=data_2015U[ data_2015U.Region=='Southern Asia']
for each in range(0,len(Western_Europe.Country)):
x = Western_Europe.Happiness_Score[each]
y = Western_Europe.Freedom[each]
plt.scatter( Western_Europe.Happiness_Score,Western_Europe.Freedom,color='red',linewidth=1)
plt.text(x, y, Western_Europe.Country[each], fontsize=12)
for each in range(0,len(North_America.Country)):
x = North_America.Happiness_Score[each]
y = North_America.Freedom[each]
plt.scatter( North_America.Happiness_Score,North_America.Freedom,color='blue',linewidth=1)
plt.text(x, y, North_America.Country[each], fontsize=12)
for each in range(0,len( Middle_East_and_Northern_Africa.Country)):
x =Middle_East_and_Northern_Africa.Happiness_Score[each]
y =Middle_East_and_Northern_Africa.Freedom[each]
plt.scatter( Middle_East_and_Northern_Africa.Happiness_Score, Middle_East_and_Northern_Africa.Freedom,color='purple',linewidth=1)
plt.text(x, y, Middle_East_and_Northern_Africa.Country[each], fontsize=12)
plt.title("Happiness Score-Freedom Scatter Plot")
plt.xlabel("Happiness Score")
plt.ylabel("Freedom")
melted = pd.melt(frame=data_2015U, id_vars='Country', value_vars=['Generosity', 'Dystopia_Residual'])
melted.loc[:10]
data_2015U1 = data_2015U.head()
data_2015U2 = data_2015U.tail()
concat_data_row = pd.concat([data_2015U1, data_2015U2], axis=0, ignore_index=True)
concat_data_row | code |
17110052/cell_16 | [
"text_html_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from subprocess import check_output
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
#try to set index to dataframe
fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank","Standard Error":"Standard_Error"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Dystopia Residual":"Dystopia_Residual","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
plt.legend(loc='upper right')
data_2015U=data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0,0],kind = 'line', color = 'red',title = 'Happiness Score',linewidth=1,grid = True,linestyle = ':')
data_2015U.Family.plot( ax=axes[0,1],kind='line' ,color='green' ,title='Family' ,linewidth=1 , grid=True ,linestyle=':' )
data_2015U.Economy.plot( ax=axes[1,0],kind='line' ,color='yellow', title='Economy',linewidth=1,grid=True ,linestyle=':' )
data_2015U.Health.plot( ax=axes[1,1],kind='line' ,color='blue', title='Health',linewidth=1,grid=True ,linestyle=':' )
# legend = puts label into plot
# label = name of label
# title = title of plot
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(50)
plt.colorbar()
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory are saved as output.
data2015=pd.read_csv('../input/2015.csv')
#fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
f,ax = plt.subplots(figsize=(20, 12))
Western_Europe=data_2015U[ data_2015U.Region=='Western Europe']
North_America=data_2015U[ data_2015U.Region=='North America']
Australian_New_Zealand=data_2015U[ data_2015U.Region=='Australia and New Zealand']
Middle_East_and_Northern_Africa=data_2015U[ data_2015U.Region=='Middle East and Northern Africa']
Latin_America_and_Caribbean=data_2015U[ data_2015U.Region=='Latin America and Caribbean']
Southeastern_Asia=data_2015U[ data_2015U.Region=='Southeastern Asia']
Central_and_Eastern_Europe=data_2015U[ data_2015U.Region=='Central and Eastern Europe']
Eastern_Asia=data_2015U[ data_2015U.Region=='Eastern_Asia']
#Sub_Saharan_Africa=data_2015U[ data_2015U.Region=='Sub Saharan Africa']
Southern_Asia=data_2015U[ data_2015U.Region=='Southern Asia']
for each in range(0,len(Western_Europe.Country)):
x = Western_Europe.Happiness_Score[each]
y = Western_Europe.Freedom[each]
plt.scatter( Western_Europe.Happiness_Score,Western_Europe.Freedom,color='red',linewidth=1)
plt.text(x, y, Western_Europe.Country[each], fontsize=12)
for each in range(0,len(North_America.Country)):
x = North_America.Happiness_Score[each]
y = North_America.Freedom[each]
plt.scatter( North_America.Happiness_Score,North_America.Freedom,color='blue',linewidth=1)
plt.text(x, y, North_America.Country[each], fontsize=12)
for each in range(0,len( Middle_East_and_Northern_Africa.Country)):
x =Middle_East_and_Northern_Africa.Happiness_Score[each]
y =Middle_East_and_Northern_Africa.Freedom[each]
plt.scatter( Middle_East_and_Northern_Africa.Happiness_Score, Middle_East_and_Northern_Africa.Freedom,color='purple',linewidth=1)
plt.text(x, y, Middle_East_and_Northern_Africa.Country[each], fontsize=12)
plt.title("Happiness Score-Freedom Scatter Plot")
plt.xlabel("Happiness Score")
plt.ylabel("Freedom")
melted = pd.melt(frame=data_2015U, id_vars='Country', value_vars=['Generosity', 'Dystopia_Residual'])
melted.loc[:10]
data_2015U1=data_2015U.head()
data_2015U2=data_2015U.tail()
concat_data_row=pd.concat([data_2015U1,data_2015U2],axis=0,ignore_index=True)
concat_data_row
data1 = data_2015U.loc[:, ['Health', 'Trust', 'Freedom']]
data1.plot() | code |
17110052/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
print(data2017.info()) | code |
17110052/cell_17 | [
"text_html_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from subprocess import check_output
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
#try to set index to dataframe
fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank","Standard Error":"Standard_Error"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Dystopia Residual":"Dystopia_Residual","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
plt.legend(loc='upper right')
data_2015U=data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0,0],kind = 'line', color = 'red',title = 'Happiness Score',linewidth=1,grid = True,linestyle = ':')
data_2015U.Family.plot( ax=axes[0,1],kind='line' ,color='green' ,title='Family' ,linewidth=1 , grid=True ,linestyle=':' )
data_2015U.Economy.plot( ax=axes[1,0],kind='line' ,color='yellow', title='Economy',linewidth=1,grid=True ,linestyle=':' )
data_2015U.Health.plot( ax=axes[1,1],kind='line' ,color='blue', title='Health',linewidth=1,grid=True ,linestyle=':' )
# legend = puts label into plot
# label = name of label
# title = title of plot
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(50)
plt.colorbar()
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory are saved as output.
data2015=pd.read_csv('../input/2015.csv')
#fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
f,ax = plt.subplots(figsize=(20, 12))
Western_Europe=data_2015U[ data_2015U.Region=='Western Europe']
North_America=data_2015U[ data_2015U.Region=='North America']
Australian_New_Zealand=data_2015U[ data_2015U.Region=='Australia and New Zealand']
Middle_East_and_Northern_Africa=data_2015U[ data_2015U.Region=='Middle East and Northern Africa']
Latin_America_and_Caribbean=data_2015U[ data_2015U.Region=='Latin America and Caribbean']
Southeastern_Asia=data_2015U[ data_2015U.Region=='Southeastern Asia']
Central_and_Eastern_Europe=data_2015U[ data_2015U.Region=='Central and Eastern Europe']
Eastern_Asia=data_2015U[ data_2015U.Region=='Eastern_Asia']
#Sub_Saharan_Africa=data_2015U[ data_2015U.Region=='Sub Saharan Africa']
Southern_Asia=data_2015U[ data_2015U.Region=='Southern Asia']
for each in range(0,len(Western_Europe.Country)):
x = Western_Europe.Happiness_Score[each]
y = Western_Europe.Freedom[each]
plt.scatter( Western_Europe.Happiness_Score,Western_Europe.Freedom,color='red',linewidth=1)
plt.text(x, y, Western_Europe.Country[each], fontsize=12)
for each in range(0,len(North_America.Country)):
x = North_America.Happiness_Score[each]
y = North_America.Freedom[each]
plt.scatter( North_America.Happiness_Score,North_America.Freedom,color='blue',linewidth=1)
plt.text(x, y, North_America.Country[each], fontsize=12)
for each in range(0,len( Middle_East_and_Northern_Africa.Country)):
x =Middle_East_and_Northern_Africa.Happiness_Score[each]
y =Middle_East_and_Northern_Africa.Freedom[each]
plt.scatter( Middle_East_and_Northern_Africa.Happiness_Score, Middle_East_and_Northern_Africa.Freedom,color='purple',linewidth=1)
plt.text(x, y, Middle_East_and_Northern_Africa.Country[each], fontsize=12)
plt.title("Happiness Score-Freedom Scatter Plot")
plt.xlabel("Happiness Score")
plt.ylabel("Freedom")
melted = pd.melt(frame=data_2015U, id_vars='Country', value_vars=['Generosity', 'Dystopia_Residual'])
melted.loc[:10]
data_2015U1=data_2015U.head()
data_2015U2=data_2015U.tail()
concat_data_row=pd.concat([data_2015U1,data_2015U2],axis=0,ignore_index=True)
concat_data_row
data1 = data_2015U.loc[:, ['Health', 'Trust', 'Freedom']]
data1.plot(subplots=True)
plt.show() | code |
17110052/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from subprocess import check_output
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
data2015.corr()
f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
#try to set index to dataframe
fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank","Standard Error":"Standard_Error"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Dystopia Residual":"Dystopia_Residual","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
plt.legend(loc='upper right')
data_2015U=data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0,0],kind = 'line', color = 'red',title = 'Happiness Score',linewidth=1,grid = True,linestyle = ':')
data_2015U.Family.plot( ax=axes[0,1],kind='line' ,color='green' ,title='Family' ,linewidth=1 , grid=True ,linestyle=':' )
data_2015U.Economy.plot( ax=axes[1,0],kind='line' ,color='yellow', title='Economy',linewidth=1,grid=True ,linestyle=':' )
data_2015U.Health.plot( ax=axes[1,1],kind='line' ,color='blue', title='Health',linewidth=1,grid=True ,linestyle=':' )
# legend = puts label into plot
# label = name of label
# title = title of plot
rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(50)
plt.colorbar()
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
# Any results you write to the current directory are saved as output.
data2015=pd.read_csv('../input/2015.csv')
#fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)
data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})
f,ax = plt.subplots(figsize=(20, 12))
Western_Europe=data_2015U[ data_2015U.Region=='Western Europe']
North_America=data_2015U[ data_2015U.Region=='North America']
Australian_New_Zealand=data_2015U[ data_2015U.Region=='Australia and New Zealand']
Middle_East_and_Northern_Africa=data_2015U[ data_2015U.Region=='Middle East and Northern Africa']
Latin_America_and_Caribbean=data_2015U[ data_2015U.Region=='Latin America and Caribbean']
Southeastern_Asia=data_2015U[ data_2015U.Region=='Southeastern Asia']
Central_and_Eastern_Europe=data_2015U[ data_2015U.Region=='Central and Eastern Europe']
Eastern_Asia=data_2015U[ data_2015U.Region=='Eastern_Asia']
#Sub_Saharan_Africa=data_2015U[ data_2015U.Region=='Sub Saharan Africa']
Southern_Asia=data_2015U[ data_2015U.Region=='Southern Asia']
for each in range(0,len(Western_Europe.Country)):
x = Western_Europe.Happiness_Score[each]
y = Western_Europe.Freedom[each]
plt.scatter( Western_Europe.Happiness_Score,Western_Europe.Freedom,color='red',linewidth=1)
plt.text(x, y, Western_Europe.Country[each], fontsize=12)
for each in range(0,len(North_America.Country)):
x = North_America.Happiness_Score[each]
y = North_America.Freedom[each]
plt.scatter( North_America.Happiness_Score,North_America.Freedom,color='blue',linewidth=1)
plt.text(x, y, North_America.Country[each], fontsize=12)
for each in range(0,len( Middle_East_and_Northern_Africa.Country)):
x =Middle_East_and_Northern_Africa.Happiness_Score[each]
y =Middle_East_and_Northern_Africa.Freedom[each]
plt.scatter( Middle_East_and_Northern_Africa.Happiness_Score, Middle_East_and_Northern_Africa.Freedom,color='purple',linewidth=1)
plt.text(x, y, Middle_East_and_Northern_Africa.Country[each], fontsize=12)
plt.title("Happiness Score-Freedom Scatter Plot")
plt.xlabel("Happiness Score")
plt.ylabel("Freedom")
melted = pd.melt(frame=data_2015U, id_vars='Country', value_vars=['Generosity', 'Dystopia_Residual'])
melted.loc[:10] | code |
17110052/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')
print(' 2015 Correlation of data ')
data2015.corr() | code |
130020662/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
hr_no_duplicates.boxplot(column='time_spend_company')
plt.show() | code |
130020662/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum() | code |
130020662/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.info() | code |
130020662/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.head(10) | code |
130020662/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
_, flier_dict = hr_no_duplicates.boxplot(column='time_spend_company', return_type='both')
outliers = [flier.get_ydata()[0] for flier in flier_dict['fliers']]
outlier_rows = hr_no_duplicates['time_spend_company'].isin(outliers)
num_outliers = outlier_rows.sum() | code |
130020662/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns | code |
130020662/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from xgboost import XGBClassifier
from xgboost import XGBRegressor
from xgboost import plot_importance
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.tree import plot_tree | code |
130020662/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
hr_no_duplicates.describe() | code |
130020662/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum() | code |
130020662/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates | code |
130020662/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
# Determine the number of rows containing outliers
# create a boxplot of the 'time_spend_company' column
_, flier_dict = hr_no_duplicates.boxplot(column='time_spend_company', return_type='both')
# get the values of the outliers
outliers = [flier.get_ydata()[0] for flier in flier_dict['fliers']]
# select the rows that contain the outliers
outlier_rows = hr_no_duplicates['time_spend_company'].isin(outliers)
# count the number of rows that contain the outliers
num_outliers = outlier_rows.sum()
percentile25 = hr_no_duplicates['time_spend_company'].quantile(0.25)
percentile75 = hr_no_duplicates['time_spend_company'].quantile(0.75)
iqr = percentile75 - percentile25
print('IQR:', iqr)
upper_limit = percentile75 + 1.5 * iqr
lower_limit = percentile25 - 1.5 * iqr
print('Lower limit:', lower_limit)
print('Upper limit:', upper_limit)
outliers = hr_no_duplicates[(hr_no_duplicates['time_spend_company'] > upper_limit) | (hr_no_duplicates['time_spend_company'] < lower_limit)]
print('Number of rows in the data containing outliers in `time_spend_company`:', len(outliers)) | code |
130020662/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape | code |
130020662/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
fig, ax = plt.subplots()
ax.boxplot(hr_no_duplicates['time_spend_company'])
whiskers = ax.lines[2:4]
whisker_values = [whisk.get_ydata()[1] for whisk in whiskers]
print('Whisker values:', whisker_values) | code |
130020662/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.describe() | code |
130020662/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
# Determine the number of rows containing outliers
# create a boxplot of the 'time_spend_company' column
_, flier_dict = hr_no_duplicates.boxplot(column='time_spend_company', return_type='both')
# get the values of the outliers
outliers = [flier.get_ydata()[0] for flier in flier_dict['fliers']]
# select the rows that contain the outliers
outlier_rows = hr_no_duplicates['time_spend_company'].isin(outliers)
# count the number of rows that contain the outliers
num_outliers = outlier_rows.sum()
sns.pairplot(hr_no_duplicates) | code |
130020662/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns | code |
90135235/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image
from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,datasets
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
torch.manual_seed(42)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.Grayscale(), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor()])
test_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.Grayscale(), transforms.ToTensor()])
dataset = datasets.ImageFolder('../input/brain-mri-images-for-brain-tumor-detection/brain_tumor_dataset', train_transforms)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
dataiter = iter(dataloader)
X,Y = dataiter.next()
classes = {0:'Negative',1:'Positive'}
fig,axes = plt.subplots(3,3,figsize=(14,14))
for i in range(3):
for j in range(3):
plt.sca(axes[i,j])
idx = np.random.randint(0,31)
image = np.moveaxis(X[idx].numpy(),0,2)
plt.title(classes[Y[idx].item()])
plt.imshow(image,cmap='gray')
plt.axis('off');
def convBlock(ni, no):
return nn.Sequential(nn.Dropout(0.2), nn.Conv2d(ni, no, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.BatchNorm2d(no), nn.MaxPool2d(2))
class BrainCancerClassifier(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(convBlock(1, 64), convBlock(64, 64), convBlock(64, 128), convBlock(128, 256), convBlock(256, 512), convBlock(512, 64), nn.Flatten(), nn.Linear(576, 256), nn.Dropout(0.3), nn.ReLU(inplace=True), nn.Linear(256, 128), nn.Dropout(0.2), nn.ReLU(inplace=True), nn.Linear(128, 64), nn.Dropout(0.2), nn.ReLU(inplace=True), nn.Linear(64, 2))
def forward(self, x):
return self.model(x)
def compute_metrics(preds, targets):
loss = nn.CrossEntropyLoss()
acc = (torch.max(preds, 1)[1] == targets).float().mean()
return (loss(preds, targets), acc)
def train_batch(model, data, optimizer):
model.train()
images, labels = data
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
loss, acc = compute_metrics(model(images), labels)
loss.backward()
optimizer.step()
return (loss.item(), acc.item())
model = BrainCancerClassifier().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
im2fmap = nn.Sequential(*list(model.model[:5].children()) + list(model.model[5][:2].children()))
def load_image(path):
image = Image.open(path)
image = test_transforms(image)
image = image.unsqueeze(0)
return image.to(device)
def get_heatmap(image_path):
model.eval()
for module in model.modules():
if isinstance(module, nn.BatchNorm2d):
module.track_running_stats = False
image = load_image(image_path)
logits = model(image)
activations = im2fmap(image)
prediction = logits.argmax(axis=1)
model.zero_grad()
logits[0, prediction].backward(retain_graph=True)
pooled_grads = model.model[5][1].weight.grad.data.mean((0, 2, 3))
for i in range(activations.shape[1]):
activations[:, i, :, :] *= -pooled_grads[i]
heatmap = torch.mean(activations, dim=1)[0].detach().to('cpu')
return (heatmap, prediction.item())
size = 224
def upsampleHeatmap(map, img):
m, M = (map.min(), map.max())
map = 255 * ((map - m) / (M - m))
map = np.uint8(map)
map = cv2.resize(map, (size, size))
map = cv2.applyColorMap(255 - map, cv2.COLORMAP_JET)
map = np.uint8(map)
map = np.uint8(map * 0.5 + img * 0.5)
return map
image_paths = ['../input/brain-mri-images-for-brain-tumor-detection/no/19 no.jpg', '../input/brain-mri-images-for-brain-tumor-detection/yes/Y1.jpg']
cols = 2
fig, axes = plt.subplots(1, cols, figsize=(8, 10))
for i in range(cols):
image = cv2.imread(image_paths[i])
image = cv2.resize(image, (224, 224))
heatmap, pred = get_heatmap(image_paths[i])
result = upsampleHeatmap(heatmap, image)
plt.sca(axes[i])
plt.imshow(result, cmap='gray')
plt.title(classes[pred])
plt.axis('off') | code |
90135235/cell_14 | [
"image_output_1.png"
] | from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,datasets
import torch
import torch.nn as nn
torch.manual_seed(42)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.Grayscale(), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor()])
test_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.Grayscale(), transforms.ToTensor()])
dataset = datasets.ImageFolder('../input/brain-mri-images-for-brain-tumor-detection/brain_tumor_dataset', train_transforms)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
def convBlock(ni, no):
return nn.Sequential(nn.Dropout(0.2), nn.Conv2d(ni, no, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.BatchNorm2d(no), nn.MaxPool2d(2))
class BrainCancerClassifier(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(convBlock(1, 64), convBlock(64, 64), convBlock(64, 128), convBlock(128, 256), convBlock(256, 512), convBlock(512, 64), nn.Flatten(), nn.Linear(576, 256), nn.Dropout(0.3), nn.ReLU(inplace=True), nn.Linear(256, 128), nn.Dropout(0.2), nn.ReLU(inplace=True), nn.Linear(128, 64), nn.Dropout(0.2), nn.ReLU(inplace=True), nn.Linear(64, 2))
def forward(self, x):
return self.model(x)
def compute_metrics(preds, targets):
loss = nn.CrossEntropyLoss()
acc = (torch.max(preds, 1)[1] == targets).float().mean()
return (loss(preds, targets), acc)
def train_batch(model, data, optimizer):
model.train()
images, labels = data
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
loss, acc = compute_metrics(model(images), labels)
loss.backward()
optimizer.step()
return (loss.item(), acc.item())
model = BrainCancerClassifier().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
im2fmap = nn.Sequential(*list(model.model[:5].children()) + list(model.model[5][:2].children()))
epochs = 20
for epoch in range(epochs):
epoch_loss = []
epoch_acc = []
for data in dataloader:
loss, acc = train_batch(model, data, optimizer)
epoch_loss.append(loss)
epoch_acc.append(acc)
print(f'Epoch: {epoch + 1}..Loss: {sum(epoch_loss) / len(epoch_loss):.3f}..Accuracy: {sum(epoch_acc) / len(epoch_acc):.3f}..') | code |
90135235/cell_5 | [
"image_output_1.png"
] | from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,datasets
import matplotlib.pyplot as plt
import numpy as np
import torch
torch.manual_seed(42)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.Grayscale(), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor()])
test_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.Grayscale(), transforms.ToTensor()])
dataset = datasets.ImageFolder('../input/brain-mri-images-for-brain-tumor-detection/brain_tumor_dataset', train_transforms)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
dataiter = iter(dataloader)
X, Y = dataiter.next()
classes = {0: 'Negative', 1: 'Positive'}
fig, axes = plt.subplots(3, 3, figsize=(14, 14))
for i in range(3):
for j in range(3):
plt.sca(axes[i, j])
idx = np.random.randint(0, 31)
image = np.moveaxis(X[idx].numpy(), 0, 2)
plt.title(classes[Y[idx].item()])
plt.imshow(image, cmap='gray')
plt.axis('off') | code |
105194300/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
d = DecisionTree(x, y, MAX_DEPTH, METRIC)
d.fit()
acc(y, d.predict(x)) | code |
105194300/cell_9 | [
"image_output_1.png"
] | import numpy as np
import seaborn as sns
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
plotDists(y) | code |
105194300/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
class Node:
def __init__(self):
self.left = None
self.right = None
self.feature_id = None
self.feature_thresh = None
self.is_leaf = False
self.result = None
self.metric = 0
self.level = -1
def set_leaf(self, result, level):
self.is_leaf = True
self.result = result
self.level = level
def set_branch(self, best_feature, best_feature_thresh, best_metric, left, right, level):
self.feature_id = best_feature
self.feature_thresh = best_feature_thresh
self.metric = best_metric
self.left = left
self.right = right
self.level = level
def traverse(self, inp):
if self.is_leaf:
return (self.result, True)
if inp[self.feature_id] <= self.feature_thresh:
return (self.left, False)
else:
return (self.right, False)
def __repr__(self):
s = ''
s += f'Level : {self.level}\n'
s += f'Leaf : {self.is_leaf}\n'
if self.is_leaf:
s += f'Result : {self.result}\n'
else:
s += f'Feature : {self.feature_id}\n'
s += f'Thresh : {self.feature_thresh}\n'
return s
class DecisionTree:
def __init__(self, x, y, MAX_LEVELS=5, metric='gini'):
self.x = np.array(x)
self.y = np.array(y)
self.data = np.concatenate((self.x, self.y.reshape(-1, 1)), axis=1)
self.n_features = self.x.shape[1]
self.classes = set(y)
self.n_classes = len(self.classes)
self.MAX_LEVELS = MAX_LEVELS
self.root = None
assert metric in ['gini'], 'Invalid metric'
self.metric = metric
def fit(self):
self.root = self.buildTree(0, self.data)
def get_metric(self, y):
_, counts = np.unique(y, return_counts=True)
total = len(y)
if self.metric == 'gini':
return 1 - np.sum((counts / total) ** 2)
elif self.metric == 'entropy':
return 0
def split_data(self, data, feature_idx, thresh):
return (data[data[:, feature_idx] <= thresh], data[data[:, feature_idx] > thresh])
def buildTree(self, level, data):
n = Node()
if level == self.MAX_LEVELS or len(set(data[:, -1])) == 1:
n.set_leaf(self.max_reps_class(data[:, -1]), level)
elif self.metric == 'gini':
best_metric = float('inf')
left_partition = None
right_partition = None
best_feature = None
best_feature_thresh = None
for feature_idx in range(self.n_features):
for thresh in set(self.data[:, feature_idx]):
left, right = self.split_data(data, feature_idx, thresh)
m = len(left) * self.get_metric(left[:, -1]) + len(right) * self.get_metric(right[:, -1])
if m < best_metric:
best_metric = m
left_partition = left
right_partition = right
best_feature = feature_idx
best_feature_thresh = thresh
n.set_branch(best_feature, best_feature_thresh, best_metric, self.buildTree(level + 1, left_partition), self.buildTree(level + 1, right_partition), level)
return n
def max_reps_class(self, y):
classes, counts = np.unique(y, return_counts=True)
return classes[np.argmax(counts)]
def predict(self, inputs, log_track=False):
if not self.root:
raise Exception('Tree Not Fit Yet!')
results = []
for inp in inputs:
res = self.root
fin = False
while not fin:
res, fin = res.traverse(inp)
results.append(int(res))
return results
d = DecisionTree(x, y, MAX_DEPTH, METRIC)
d.fit()
m = DecisionTreeClassifier(max_depth=MAX_DEPTH, criterion=METRIC)
m.fit(x, y)
acc(y, d.predict(x))
acc(y, m.predict(x))
print(acc(y, m.predict(x)) - acc(y, d.predict(x))) | code |
105194300/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
class Node:
def __init__(self):
self.left = None
self.right = None
self.feature_id = None
self.feature_thresh = None
self.is_leaf = False
self.result = None
self.metric = 0
self.level = -1
def set_leaf(self, result, level):
self.is_leaf = True
self.result = result
self.level = level
def set_branch(self, best_feature, best_feature_thresh, best_metric, left, right, level):
self.feature_id = best_feature
self.feature_thresh = best_feature_thresh
self.metric = best_metric
self.left = left
self.right = right
self.level = level
def traverse(self, inp):
if self.is_leaf:
return (self.result, True)
if inp[self.feature_id] <= self.feature_thresh:
return (self.left, False)
else:
return (self.right, False)
def __repr__(self):
s = ''
s += f'Level : {self.level}\n'
s += f'Leaf : {self.is_leaf}\n'
if self.is_leaf:
s += f'Result : {self.result}\n'
else:
s += f'Feature : {self.feature_id}\n'
s += f'Thresh : {self.feature_thresh}\n'
return s
class DecisionTree:
def __init__(self, x, y, MAX_LEVELS=5, metric='gini'):
self.x = np.array(x)
self.y = np.array(y)
self.data = np.concatenate((self.x, self.y.reshape(-1, 1)), axis=1)
self.n_features = self.x.shape[1]
self.classes = set(y)
self.n_classes = len(self.classes)
self.MAX_LEVELS = MAX_LEVELS
self.root = None
assert metric in ['gini'], 'Invalid metric'
self.metric = metric
def fit(self):
self.root = self.buildTree(0, self.data)
def get_metric(self, y):
_, counts = np.unique(y, return_counts=True)
total = len(y)
if self.metric == 'gini':
return 1 - np.sum((counts / total) ** 2)
elif self.metric == 'entropy':
return 0
def split_data(self, data, feature_idx, thresh):
return (data[data[:, feature_idx] <= thresh], data[data[:, feature_idx] > thresh])
def buildTree(self, level, data):
n = Node()
if level == self.MAX_LEVELS or len(set(data[:, -1])) == 1:
n.set_leaf(self.max_reps_class(data[:, -1]), level)
elif self.metric == 'gini':
best_metric = float('inf')
left_partition = None
right_partition = None
best_feature = None
best_feature_thresh = None
for feature_idx in range(self.n_features):
for thresh in set(self.data[:, feature_idx]):
left, right = self.split_data(data, feature_idx, thresh)
m = len(left) * self.get_metric(left[:, -1]) + len(right) * self.get_metric(right[:, -1])
if m < best_metric:
best_metric = m
left_partition = left
right_partition = right
best_feature = feature_idx
best_feature_thresh = thresh
n.set_branch(best_feature, best_feature_thresh, best_metric, self.buildTree(level + 1, left_partition), self.buildTree(level + 1, right_partition), level)
return n
def max_reps_class(self, y):
classes, counts = np.unique(y, return_counts=True)
return classes[np.argmax(counts)]
def predict(self, inputs, log_track=False):
if not self.root:
raise Exception('Tree Not Fit Yet!')
results = []
for inp in inputs:
res = self.root
fin = False
while not fin:
res, fin = res.traverse(inp)
results.append(int(res))
return results
d = DecisionTree(x, y, MAX_DEPTH, METRIC)
d.fit()
m = DecisionTreeClassifier(max_depth=MAX_DEPTH, criterion=METRIC)
m.fit(x, y)
acc(y, d.predict(x))
acc(y, m.predict(x))
d.predict([x[1]], True) | code |
105194300/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
class Node:
def __init__(self):
self.left = None
self.right = None
self.feature_id = None
self.feature_thresh = None
self.is_leaf = False
self.result = None
self.metric = 0
self.level = -1
def set_leaf(self, result, level):
self.is_leaf = True
self.result = result
self.level = level
def set_branch(self, best_feature, best_feature_thresh, best_metric, left, right, level):
self.feature_id = best_feature
self.feature_thresh = best_feature_thresh
self.metric = best_metric
self.left = left
self.right = right
self.level = level
def traverse(self, inp):
if self.is_leaf:
return (self.result, True)
if inp[self.feature_id] <= self.feature_thresh:
return (self.left, False)
else:
return (self.right, False)
def __repr__(self):
s = ''
s += f'Level : {self.level}\n'
s += f'Leaf : {self.is_leaf}\n'
if self.is_leaf:
s += f'Result : {self.result}\n'
else:
s += f'Feature : {self.feature_id}\n'
s += f'Thresh : {self.feature_thresh}\n'
return s
class DecisionTree:
def __init__(self, x, y, MAX_LEVELS=5, metric='gini'):
self.x = np.array(x)
self.y = np.array(y)
self.data = np.concatenate((self.x, self.y.reshape(-1, 1)), axis=1)
self.n_features = self.x.shape[1]
self.classes = set(y)
self.n_classes = len(self.classes)
self.MAX_LEVELS = MAX_LEVELS
self.root = None
assert metric in ['gini'], 'Invalid metric'
self.metric = metric
def fit(self):
self.root = self.buildTree(0, self.data)
def get_metric(self, y):
_, counts = np.unique(y, return_counts=True)
total = len(y)
if self.metric == 'gini':
return 1 - np.sum((counts / total) ** 2)
elif self.metric == 'entropy':
return 0
def split_data(self, data, feature_idx, thresh):
return (data[data[:, feature_idx] <= thresh], data[data[:, feature_idx] > thresh])
def buildTree(self, level, data):
n = Node()
if level == self.MAX_LEVELS or len(set(data[:, -1])) == 1:
n.set_leaf(self.max_reps_class(data[:, -1]), level)
elif self.metric == 'gini':
best_metric = float('inf')
left_partition = None
right_partition = None
best_feature = None
best_feature_thresh = None
for feature_idx in range(self.n_features):
for thresh in set(self.data[:, feature_idx]):
left, right = self.split_data(data, feature_idx, thresh)
m = len(left) * self.get_metric(left[:, -1]) + len(right) * self.get_metric(right[:, -1])
if m < best_metric:
best_metric = m
left_partition = left
right_partition = right
best_feature = feature_idx
best_feature_thresh = thresh
n.set_branch(best_feature, best_feature_thresh, best_metric, self.buildTree(level + 1, left_partition), self.buildTree(level + 1, right_partition), level)
return n
def max_reps_class(self, y):
classes, counts = np.unique(y, return_counts=True)
return classes[np.argmax(counts)]
def predict(self, inputs, log_track=False):
if not self.root:
raise Exception('Tree Not Fit Yet!')
results = []
for inp in inputs:
res = self.root
fin = False
while not fin:
res, fin = res.traverse(inp)
results.append(int(res))
return results
m = DecisionTreeClassifier(max_depth=MAX_DEPTH, criterion=METRIC)
m.fit(x, y)
plotDists(m.predict(x)) | code |
105194300/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
class Node:
def __init__(self):
self.left = None
self.right = None
self.feature_id = None
self.feature_thresh = None
self.is_leaf = False
self.result = None
self.metric = 0
self.level = -1
def set_leaf(self, result, level):
self.is_leaf = True
self.result = result
self.level = level
def set_branch(self, best_feature, best_feature_thresh, best_metric, left, right, level):
self.feature_id = best_feature
self.feature_thresh = best_feature_thresh
self.metric = best_metric
self.left = left
self.right = right
self.level = level
def traverse(self, inp):
if self.is_leaf:
return (self.result, True)
if inp[self.feature_id] <= self.feature_thresh:
return (self.left, False)
else:
return (self.right, False)
def __repr__(self):
s = ''
s += f'Level : {self.level}\n'
s += f'Leaf : {self.is_leaf}\n'
if self.is_leaf:
s += f'Result : {self.result}\n'
else:
s += f'Feature : {self.feature_id}\n'
s += f'Thresh : {self.feature_thresh}\n'
return s
class DecisionTree:
def __init__(self, x, y, MAX_LEVELS=5, metric='gini'):
self.x = np.array(x)
self.y = np.array(y)
self.data = np.concatenate((self.x, self.y.reshape(-1, 1)), axis=1)
self.n_features = self.x.shape[1]
self.classes = set(y)
self.n_classes = len(self.classes)
self.MAX_LEVELS = MAX_LEVELS
self.root = None
assert metric in ['gini'], 'Invalid metric'
self.metric = metric
def fit(self):
self.root = self.buildTree(0, self.data)
def get_metric(self, y):
_, counts = np.unique(y, return_counts=True)
total = len(y)
if self.metric == 'gini':
return 1 - np.sum((counts / total) ** 2)
elif self.metric == 'entropy':
return 0
def split_data(self, data, feature_idx, thresh):
return (data[data[:, feature_idx] <= thresh], data[data[:, feature_idx] > thresh])
def buildTree(self, level, data):
n = Node()
if level == self.MAX_LEVELS or len(set(data[:, -1])) == 1:
n.set_leaf(self.max_reps_class(data[:, -1]), level)
elif self.metric == 'gini':
best_metric = float('inf')
left_partition = None
right_partition = None
best_feature = None
best_feature_thresh = None
for feature_idx in range(self.n_features):
for thresh in set(self.data[:, feature_idx]):
left, right = self.split_data(data, feature_idx, thresh)
m = len(left) * self.get_metric(left[:, -1]) + len(right) * self.get_metric(right[:, -1])
if m < best_metric:
best_metric = m
left_partition = left
right_partition = right
best_feature = feature_idx
best_feature_thresh = thresh
n.set_branch(best_feature, best_feature_thresh, best_metric, self.buildTree(level + 1, left_partition), self.buildTree(level + 1, right_partition), level)
return n
def max_reps_class(self, y):
classes, counts = np.unique(y, return_counts=True)
return classes[np.argmax(counts)]
def predict(self, inputs, log_track=False):
if not self.root:
raise Exception('Tree Not Fit Yet!')
results = []
for inp in inputs:
res = self.root
fin = False
while not fin:
res, fin = res.traverse(inp)
results.append(int(res))
return results
m = DecisionTreeClassifier(max_depth=MAX_DEPTH, criterion=METRIC)
m.fit(x, y) | code |
105194300/cell_28 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
class Node:
def __init__(self):
self.left = None
self.right = None
self.feature_id = None
self.feature_thresh = None
self.is_leaf = False
self.result = None
self.metric = 0
self.level = -1
def set_leaf(self, result, level):
self.is_leaf = True
self.result = result
self.level = level
def set_branch(self, best_feature, best_feature_thresh, best_metric, left, right, level):
self.feature_id = best_feature
self.feature_thresh = best_feature_thresh
self.metric = best_metric
self.left = left
self.right = right
self.level = level
def traverse(self, inp):
if self.is_leaf:
return (self.result, True)
if inp[self.feature_id] <= self.feature_thresh:
return (self.left, False)
else:
return (self.right, False)
def __repr__(self):
s = ''
s += f'Level : {self.level}\n'
s += f'Leaf : {self.is_leaf}\n'
if self.is_leaf:
s += f'Result : {self.result}\n'
else:
s += f'Feature : {self.feature_id}\n'
s += f'Thresh : {self.feature_thresh}\n'
return s
class DecisionTree:
def __init__(self, x, y, MAX_LEVELS=5, metric='gini'):
self.x = np.array(x)
self.y = np.array(y)
self.data = np.concatenate((self.x, self.y.reshape(-1, 1)), axis=1)
self.n_features = self.x.shape[1]
self.classes = set(y)
self.n_classes = len(self.classes)
self.MAX_LEVELS = MAX_LEVELS
self.root = None
assert metric in ['gini'], 'Invalid metric'
self.metric = metric
def fit(self):
self.root = self.buildTree(0, self.data)
def get_metric(self, y):
_, counts = np.unique(y, return_counts=True)
total = len(y)
if self.metric == 'gini':
return 1 - np.sum((counts / total) ** 2)
elif self.metric == 'entropy':
return 0
def split_data(self, data, feature_idx, thresh):
return (data[data[:, feature_idx] <= thresh], data[data[:, feature_idx] > thresh])
def buildTree(self, level, data):
n = Node()
if level == self.MAX_LEVELS or len(set(data[:, -1])) == 1:
n.set_leaf(self.max_reps_class(data[:, -1]), level)
elif self.metric == 'gini':
best_metric = float('inf')
left_partition = None
right_partition = None
best_feature = None
best_feature_thresh = None
for feature_idx in range(self.n_features):
for thresh in set(self.data[:, feature_idx]):
left, right = self.split_data(data, feature_idx, thresh)
m = len(left) * self.get_metric(left[:, -1]) + len(right) * self.get_metric(right[:, -1])
if m < best_metric:
best_metric = m
left_partition = left
right_partition = right
best_feature = feature_idx
best_feature_thresh = thresh
n.set_branch(best_feature, best_feature_thresh, best_metric, self.buildTree(level + 1, left_partition), self.buildTree(level + 1, right_partition), level)
return n
def max_reps_class(self, y):
classes, counts = np.unique(y, return_counts=True)
return classes[np.argmax(counts)]
def predict(self, inputs, log_track=False):
if not self.root:
raise Exception('Tree Not Fit Yet!')
results = []
for inp in inputs:
res = self.root
fin = False
while not fin:
res, fin = res.traverse(inp)
results.append(int(res))
return results
d = DecisionTree(x, y, MAX_DEPTH, METRIC)
d.fit()
m = DecisionTreeClassifier(max_depth=MAX_DEPTH, criterion=METRIC)
m.fit(x, y)
acc(y, d.predict(x))
acc(y, m.predict(x))
fig, ax = plt.subplots(figsize=(20, 16))
_ = tree.plot_tree(m, ax=ax) | code |
105194300/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.datasets import make_classification
import pandas as pd
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
x, y = make_classification(NUM_POINTS, NUM_FEATURES, n_informative=NUM_FEATURES // 2, n_classes=NUM_CLASSES)
df = pd.DataFrame(x)
df['y'] = y
df.head() | code |
105194300/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
d = DecisionTree(x, y, MAX_DEPTH, METRIC)
d.fit()
plotDists(d.predict(x)) | code |
105194300/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import numpy as np
import seaborn as sns
NUM_FEATURES = 10
NUM_CLASSES = 3
NUM_POINTS = 100
MAX_DEPTH = 5
METRIC = 'gini'
def plotDists(y):
return
def acc(true, pred):
assert len(true) == len(pred), 'Truth and Pred Lengths not same'
true = np.array(true)
pred = np.array(pred).astype(np.int32)
return np.sum(true == pred) / len(true)
class Node:
def __init__(self):
self.left = None
self.right = None
self.feature_id = None
self.feature_thresh = None
self.is_leaf = False
self.result = None
self.metric = 0
self.level = -1
def set_leaf(self, result, level):
self.is_leaf = True
self.result = result
self.level = level
def set_branch(self, best_feature, best_feature_thresh, best_metric, left, right, level):
self.feature_id = best_feature
self.feature_thresh = best_feature_thresh
self.metric = best_metric
self.left = left
self.right = right
self.level = level
def traverse(self, inp):
if self.is_leaf:
return (self.result, True)
if inp[self.feature_id] <= self.feature_thresh:
return (self.left, False)
else:
return (self.right, False)
def __repr__(self):
s = ''
s += f'Level : {self.level}\n'
s += f'Leaf : {self.is_leaf}\n'
if self.is_leaf:
s += f'Result : {self.result}\n'
else:
s += f'Feature : {self.feature_id}\n'
s += f'Thresh : {self.feature_thresh}\n'
return s
class DecisionTree:
def __init__(self, x, y, MAX_LEVELS=5, metric='gini'):
self.x = np.array(x)
self.y = np.array(y)
self.data = np.concatenate((self.x, self.y.reshape(-1, 1)), axis=1)
self.n_features = self.x.shape[1]
self.classes = set(y)
self.n_classes = len(self.classes)
self.MAX_LEVELS = MAX_LEVELS
self.root = None
assert metric in ['gini'], 'Invalid metric'
self.metric = metric
def fit(self):
self.root = self.buildTree(0, self.data)
def get_metric(self, y):
_, counts = np.unique(y, return_counts=True)
total = len(y)
if self.metric == 'gini':
return 1 - np.sum((counts / total) ** 2)
elif self.metric == 'entropy':
return 0
def split_data(self, data, feature_idx, thresh):
return (data[data[:, feature_idx] <= thresh], data[data[:, feature_idx] > thresh])
def buildTree(self, level, data):
n = Node()
if level == self.MAX_LEVELS or len(set(data[:, -1])) == 1:
n.set_leaf(self.max_reps_class(data[:, -1]), level)
elif self.metric == 'gini':
best_metric = float('inf')
left_partition = None
right_partition = None
best_feature = None
best_feature_thresh = None
for feature_idx in range(self.n_features):
for thresh in set(self.data[:, feature_idx]):
left, right = self.split_data(data, feature_idx, thresh)
m = len(left) * self.get_metric(left[:, -1]) + len(right) * self.get_metric(right[:, -1])
if m < best_metric:
best_metric = m
left_partition = left
right_partition = right
best_feature = feature_idx
best_feature_thresh = thresh
n.set_branch(best_feature, best_feature_thresh, best_metric, self.buildTree(level + 1, left_partition), self.buildTree(level + 1, right_partition), level)
return n
def max_reps_class(self, y):
classes, counts = np.unique(y, return_counts=True)
return classes[np.argmax(counts)]
def predict(self, inputs, log_track=False):
if not self.root:
raise Exception('Tree Not Fit Yet!')
results = []
for inp in inputs:
res = self.root
fin = False
while not fin:
res, fin = res.traverse(inp)
results.append(int(res))
return results
m = DecisionTreeClassifier(max_depth=MAX_DEPTH, criterion=METRIC)
m.fit(x, y)
acc(y, m.predict(x)) | code |
16115331/cell_4 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for tokenized_sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(tokenized_sentence))
return word_tokenized
def np_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'NP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
def vp_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'VP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
text = open('../input/the_wizard_of_oz.txt', encoding='utf-8').read().lower()
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(sentence))
print(word_tokenized[10])
print(len(word_tokenized)) | code |
16115331/cell_6 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for tokenized_sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(tokenized_sentence))
return word_tokenized
def np_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'NP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
def vp_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'VP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
text = open('../input/the_wizard_of_oz.txt', encoding='utf-8').read().lower()
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(sentence))
pos_tagged_text = list()
for sentence in word_tokenized:
pos_tagged_text.append(pos_tag(sentence))
chunk_grammar = 'NP: {<DT>?<JJ>*<NN>}'
vp_chunk_grammar = 'VP: {<VB.*><DT>?<JJ>*<NN><RB.?>?}'
chunk_parser = RegexpParser(chunk_grammar)
vp_chunk_parser = RegexpParser(vp_chunk_grammar)
chunked_sentence = chunk_parser.parse(pos_tagged_text[10])
print(chunked_sentence)
vp_chunked_sentence = vp_chunk_parser.parse(pos_tagged_text[10])
print(vp_chunked_sentence) | code |
16115331/cell_7 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for tokenized_sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(tokenized_sentence))
return word_tokenized
def np_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'NP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
def vp_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'VP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
text = open('../input/the_wizard_of_oz.txt', encoding='utf-8').read().lower()
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(sentence))
pos_tagged_text = list()
for sentence in word_tokenized:
pos_tagged_text.append(pos_tag(sentence))
chunk_grammar = 'NP: {<DT>?<JJ>*<NN>}'
vp_chunk_grammar = 'VP: {<VB.*><DT>?<JJ>*<NN><RB.?>?}'
chunk_parser = RegexpParser(chunk_grammar)
vp_chunk_parser = RegexpParser(vp_chunk_grammar)
chunked_sentence = chunk_parser.parse(pos_tagged_text[10])
vp_chunked_sentence = vp_chunk_parser.parse(pos_tagged_text[10])
np_chunked_sentences = list()
vp_chunked_sentences = list()
for sentence in pos_tagged_text:
np_chunked_sentences.append(chunk_parser.parse(sentence))
vp_chunked_sentences.append(vp_chunk_parser.parse(sentence))
print(np_chunked_sentences[222])
print(vp_chunked_sentences[222]) | code |
16115331/cell_8 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for tokenized_sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(tokenized_sentence))
return word_tokenized
def np_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'NP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
def vp_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'VP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
text = open('../input/the_wizard_of_oz.txt', encoding='utf-8').read().lower()
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(sentence))
pos_tagged_text = list()
for sentence in word_tokenized:
pos_tagged_text.append(pos_tag(sentence))
chunk_grammar = 'NP: {<DT>?<JJ>*<NN>}'
vp_chunk_grammar = 'VP: {<VB.*><DT>?<JJ>*<NN><RB.?>?}'
chunk_parser = RegexpParser(chunk_grammar)
vp_chunk_parser = RegexpParser(vp_chunk_grammar)
chunked_sentence = chunk_parser.parse(pos_tagged_text[10])
vp_chunked_sentence = vp_chunk_parser.parse(pos_tagged_text[10])
np_chunked_sentences = list()
vp_chunked_sentences = list()
for sentence in pos_tagged_text:
np_chunked_sentences.append(chunk_parser.parse(sentence))
vp_chunked_sentences.append(vp_chunk_parser.parse(sentence))
most_common_np_chunks = np_chunk_counter(np_chunked_sentences)
print('NP chunks')
print(most_common_np_chunks)
most_common_vp_chunks = vp_chunk_counter(vp_chunked_sentences)
print('VP chunks')
print(most_common_vp_chunks) | code |
16115331/cell_3 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for tokenized_sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(tokenized_sentence))
return word_tokenized
def np_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'NP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
def vp_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'VP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
text = open('../input/the_wizard_of_oz.txt', encoding='utf-8').read().lower()
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
print(sentence_tokenized[10])
print(len(sentence_tokenized)) | code |
16115331/cell_5 | [
"text_plain_output_1.png"
] | from collections import Counter
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
import os
import nltk
from nltk import pos_tag, RegexpParser
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize
from collections import Counter
def word_sentence_tokenize(text):
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for tokenized_sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(tokenized_sentence))
return word_tokenized
def np_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'NP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
def vp_chunk_counter(chunked_sentences):
chunks = list()
for chunked_sentence in chunked_sentences:
for subtree in chunked_sentence.subtrees(filter=lambda t: t.label() == 'VP'):
chunks.append(tuple(subtree))
chunk_counter = Counter()
for chunk in chunks:
chunk_counter[chunk] += 1
return chunk_counter.most_common(30)
text = open('../input/the_wizard_of_oz.txt', encoding='utf-8').read().lower()
sentence_tokenizer = PunktSentenceTokenizer(text)
sentence_tokenized = sentence_tokenizer.tokenize(text)
word_tokenized = list()
for sentence in sentence_tokenized:
word_tokenized.append(word_tokenize(sentence))
pos_tagged_text = list()
for sentence in word_tokenized:
pos_tagged_text.append(pos_tag(sentence))
print(pos_tagged_text[10]) | code |
2002501/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] = x.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
x['Embarked'] = x.apply(replace_1, axis=1)
test = pd.read_csv('../input/test.csv')
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
test['Sex'] = test.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
test['Embarked'] = test.apply(replace_1, axis=1)
test.dropna()
features = ['Pclass', 'Sex', 'Age', 'Embarked']
test = test[features]
test.isnull().any()
test = test.dropna()
test = test.dropna()
test.head() | code |
2002501/cell_13 | [
"text_html_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] = x.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
x['Embarked'] = x.apply(replace_1, axis=1)
result = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)
result.fit(x, outcome) | code |
2002501/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any() | code |
2002501/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
outcome.shape | code |
2002501/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape | code |
2002501/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
df.shape
d = df[['PassengerId']].copy()
d.shape | code |
2002501/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] = x.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
x['Embarked'] = x.apply(replace_1, axis=1)
test = pd.read_csv('../input/test.csv')
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
test['Sex'] = test.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
test['Embarked'] = test.apply(replace_1, axis=1)
test.dropna()
features = ['Pclass', 'Sex', 'Age', 'Embarked']
test = test[features]
test.isnull().any() | code |
2002501/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] = x.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
x['Embarked'] = x.apply(replace_1, axis=1)
test = pd.read_csv('../input/test.csv')
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
test['Sex'] = test.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
test['Embarked'] = test.apply(replace_1, axis=1)
test.dropna()
test.head() | code |
2002501/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
df.shape
d = df[['PassengerId']].copy()
d.shape
d.head() | code |
2002501/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
outcome | code |
2002501/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
2002501/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.head() | code |
2002501/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
df.shape | code |
2002501/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.shape
outcome = df[['Survived']].copy()
features = ['Pclass', 'Age', 'Sex', 'Cabin', 'Embarked']
x = df[features].copy()
del x['Cabin']
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
x['Sex'] = x.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
x['Embarked'] = x.apply(replace_1, axis=1)
result = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)
result.fit(x, outcome)
test = pd.read_csv('../input/test.csv')
def replace(x):
Sex = x['Sex']
if Sex in ['female']:
return 0
else:
return 1
test['Sex'] = test.apply(replace, axis=1)
def replace_1(x):
Embarked = x['Embarked']
if Embarked in ['E']:
return 0
elif Embarked in ['C']:
return 1
else:
return 2
test['Embarked'] = test.apply(replace_1, axis=1)
test.dropna()
features = ['Pclass', 'Sex', 'Age', 'Embarked']
test = test[features]
test.isnull().any()
test = test.dropna()
test = test.dropna()
outcomes = result.predict(test)
outcomes | code |
2002501/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
df.isnull().any()
df = df.dropna()
df.head() | code |
72100024/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(columns=['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
newList = list(missing_cols.index)
newList.append('SalePrice')
train[newList].corr()
cols_to_be_removed = ['LotFrontage', 'GarageYrBlt', 'MasVnrArea']
train.drop(columns=cols_to_be_removed, inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
print(f'Columns with missing values: {len(missing_cols)}')
missing_cols.sort_values(ascending=False) | code |
72100024/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.info() | code |
72100024/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
print('No. of columns with missing values:', len(missing_cols))
missing_cols.sort_values(ascending=False) | code |
72100024/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(columns=['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.info() | code |
72100024/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(columns=['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
newList = list(missing_cols.index)
newList.append('SalePrice')
train[newList].corr()
cols_to_be_removed = ['LotFrontage', 'GarageYrBlt', 'MasVnrArea']
train.drop(columns=cols_to_be_removed, inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
print(f'No. of columns with missing values: {len(missing_cols)}')
missing_cols.sort_values(ascending=False) | code |
72100024/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72100024/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(columns=['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
newList = list(missing_cols.index)
newList.append('SalePrice')
train[newList].corr()
cols_to_be_removed = ['LotFrontage', 'GarageYrBlt', 'MasVnrArea']
train.drop(columns=cols_to_be_removed, inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
ordinal_cols = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'HeatingQC', 'KitchenQual', 'GarageQual', 'GarageCond']
for item in range(len(ordinal_cols)):
sns.barplot(x=ordinal_cols[item], y='SalePrice', data=train)
plt.show() | code |
72100024/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(columns=['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace=True)
sns.barplot(x='FireplaceQu', y='SalePrice', data=train) | code |
72100024/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(columns=['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
newList = list(missing_cols.index)
newList.append('SalePrice')
train[newList].corr()
cols_to_be_removed = ['LotFrontage', 'GarageYrBlt', 'MasVnrArea']
train.drop(columns=cols_to_be_removed, inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
print('No. of columns with missing values:', len(missing_cols))
missing_cols.sort_values(ascending=False) | code |
72100024/cell_3 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape | code |
72100024/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train-dataset/train.csv')
train.shape
train.drop(columns=['Id'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
train.drop(columns=['PoolQC', 'MiscFeature', 'Alley', 'Fence'], inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
missing_cols.sort_values(ascending=False)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
newList = list(missing_cols.index)
newList.append('SalePrice')
train[newList].corr()
cols_to_be_removed = ['LotFrontage', 'GarageYrBlt', 'MasVnrArea']
train.drop(columns=cols_to_be_removed, inplace=True)
missing_cols = train.isna().sum()
missing_cols = missing_cols[missing_cols != 0]
print('No. of columns with missing values:', len(missing_cols))
missing_cols.sort_values(ascending=False) | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.