path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122262213/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_te... | code |
73097582/cell_34 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file... | code |
73097582/cell_30 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file... | code |
73097582/cell_33 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import glob
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file... | code |
73097582/cell_44 | [
"text_plain_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_d... | code |
73097582/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import glob
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file, ignore_index=True)... | code |
73097582/cell_6 | [
"text_plain_output_1.png"
] | import glob
import os
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files | code |
73097582/cell_40 | [
"text_html_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_d... | code |
73097582/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import glob
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_d... | code |
73097582/cell_11 | [
"text_plain_output_1.png"
] | import glob
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file, ignore_index=True)... | code |
73097582/cell_7 | [
"image_output_1.png"
] | import glob
import os
import pandas as pd
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file, ignore_index=True)
merged_df.head() | code |
73097582/cell_8 | [
"text_plain_output_1.png"
] | import glob
import os
import pandas as pd
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file, ignore_index=True)
merged_df.tail() | code |
73097582/cell_16 | [
"text_html_output_1.png"
] | import glob
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file, ignore_index=True)... | code |
73097582/cell_31 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file... | code |
73097582/cell_24 | [
"text_plain_output_1.png"
] | import glob
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file... | code |
73097582/cell_14 | [
"text_html_output_1.png"
] | import glob
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file, ignore_index=True)... | code |
73097582/cell_22 | [
"text_plain_output_1.png"
] | import glob
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file, ignore_index=True)... | code |
73097582/cell_27 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import glob
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file... | code |
73097582/cell_36 | [
"text_html_output_1.png"
] | import glob
import numpy as np
import os
import pandas as pd
import scipy.stats as stats
path = '../input/cyclistic-trip-data'
all_files = glob.glob(os.path.join(path, '*.csv'))
all_files
merged_df = pd.DataFrame()
for f in all_files:
df_each_file = pd.read_csv(f)
merged_df = merged_df.append(df_each_file... | code |
49130544/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/creditcard/creditcard.csv')
fraud = data[data['Class'] == 1]
normal = data[data['Class'] == 0]
f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
f.suptitle('Amount per transaction by class')
bins = 50
ax1.hist(fraud.Amount, bins=bins)
ax... | code |
49130544/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/creditcard/creditcard.csv')
fraud = data[data['Class'] == 1]
normal = data... | code |
49130544/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/creditcard/creditcard.csv')
fraud = data[data['Class'] == 1]
normal = data[data['Class'] == 0]
f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
f.suptitle('Amount per tra... | code |
49130544/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/creditcard/creditcard.csv')
data.head() | code |
49130544/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from statistics import mean
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/creditcard/creditcard.csv')
fraud = data[data['Class'] == 1]
normal = data[data['Class'] == 0]
f, (ax1, ax2) = plt.subplots(2, 1, sharex=Tru... | code |
49130544/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/creditcard/creditcard.csv')
fraud = data[data['Class'] == 1]
normal = data[data['Class'] == 0]
f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
f.suptitle('Amount per tra... | code |
49130544/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import IsolationForest
from sklearn.metrics import classification_report,accuracy_score
from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/creditcar... | code |
49130544/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/creditcard/creditcard.csv')
fraud = data[data['Class'] == 1]
normal = data[data['Class'] == 0]
f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
f.suptitle('Amount per transaction by class')
bins = 50
ax1.hist(fraud.Amount, bins = bins)
... | code |
2017954/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
pd.isnull(train).sum()
pd.isnull(test).sum()
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = te... | code |
2017954/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
pd.isnull(train).sum() | code |
2017954/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
tes... | code |
2017954/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2017954/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_32 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
pd.isnull(train).sum()
pd.isnull(test).sum()
test_ID = test.PassengerId
train = train.drop... | code |
2017954/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
age_mean_t... | code |
2017954/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_31 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
tes... | code |
2017954/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = test.drop(['Name', 'Ticket'], axis=1)
train.drop... | code |
2017954/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
pd.isnull(train).sum()
pd.isnull(test).sum()
test_ID = test.PassengerId
train = train.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test = te... | code |
2017954/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
pd.isnull(train).sum()
pd.isnull(test).sum() | code |
90120622/cell_21 | [
"text_html_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df['time'] = df['time'].map(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d %X'))
df['day'] = df['time'].map(lambda x: x.strftime... | code |
90120622/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df.head() | code |
90120622/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df.head() | code |
90120622/cell_23 | [
"image_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df['time'] = df['time'].map(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d %X'))
df['day'] = df['time'].map(lambda x: x.strftime... | code |
90120622/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
print(df.x.unique(), df.y.unique(), df.direction.unique()) | code |
90120622/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
fig, axes = plt.subplots(nrows=2,ncols=2)
fig.set_size_inches(12, 10)
sns.boxplot(data=df,y="congestion",x="y",ax=axes[0][0])
sns.boxplot(data=df,y="congestion",x="x",o... | code |
90120622/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df['xy'] = list(zip(df['x'], df['y']))
df['xydir'] = list(zip(df['x'], df['y'], df['direction']))
df['xy'].unique() | code |
90120622/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
fig, axes = plt.subplots(nrows=2, ncols=2)
fig.set_size_inches(12, 10)
sns.boxplot(data=df, y='congestion', x='y', ax=axes[0][0])
sns.boxplot(data=df, y='congestion', x... | code |
90120622/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
for i in df['xy'].unique():
print(i, ':', df.loc[df['xy'] == i, 'direction'].unique()) | code |
334788/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
dficd = pd.read_csv('../input/Icd10Code.csv') | code |
104117596/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
model.score(X_test, y_test)
arr = model.predict(X_test)
arr2 = []
for x in arr:
if x == 1:
arr2.append(x)
print(len(arr2) / len(arr)) | code |
104117596/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104117596/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train) | code |
104117596/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
model.score(X_test, y_test) | code |
73086698/cell_6 | [
"text_html_output_1.png"
] | from surprise import Dataset,Reader,SVD
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
columns
r... | code |
73086698/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 |
73086698/cell_7 | [
"text_html_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns... | code |
73086698/cell_8 | [
"text_html_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns... | code |
73086698/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from surprise import Dataset,Reader,SVD
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns = ratings.movieId.unique()
print(len... | code |
73086698/cell_5 | [
"text_html_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns... | code |
122262131/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import cufflinks as cs
import plotly.express as px
import plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot | code |
122262131/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_44 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['Quantity']
df1['Price'] = df1['Sales'] / df1['Quantity']
df1['Cost Per Unit'] = df1['Price'] - df1['Profit/... | code |
122262131/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.head() | code |
122262131/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/U... | code |
122262131/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/U... | code |
122262131/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/U... | code |
122262131/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1.nunique()
df1.isna().sum() | code |
122262131/cell_32 | [
"text_html_output_2.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape | code |
122262131/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1.nunique() | code |
122262131/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['... | code |
122262131/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1.nunique()
df1.isna().sum()
df1['Profit/Unit'] = df1['Profit'] / df1['Quantity']
df1['Price'] = df1['Sales'] / df1['Quantity']
df1['Cost Per Unit'] = df1['Price'] - df1['Profit/... | code |
122262131/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.info() | code |
122262131/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns
df1['Order Date'] = pd.to_datetime(df1['Order Date'])
df1['Ship Date'] = pd.to_datetime(df1['Ship Date'])
df1.nunique()
df1.isna().sum()
df1['Profit/U... | code |
122262131/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('/kaggle/input/walmart-sales-analysis/Walmart.csv', index_col='Order ID')
df1.shape
df1.columns | code |
130022960/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
b = asus['News... | code |
130022960/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
plt.figure(fig... | code |
130022960/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape | code |
130022960/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
b = asus['News... | code |
130022960/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.describe() | code |
130022960/cell_2 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
130022960/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
plt.figure(fig... | code |
130022960/cell_19 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
b = asus['Newspaper Ad Budget ($)'].quantile(0.98)
asus_new = asus[asu... | code |
130022960/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0']) | code |
130022960/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
b = asus['Newspaper Ad Budget ($)'].quantile(0.98)
asus_new = asus[asu... | code |
130022960/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
b = asus['Newspaper Ad Budget ($)'].quantile(0.98)
asus_new = asus[asu... | code |
130022960/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.shape
asus.drop(columns=['Unnamed: 0'])
b = asus['Newspaper Ad Budget ($)'].quantile(0.98)
asus_new = asus[asu... | code |
130022960/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
asus = pd.DataFrame(pd.read_csv('/kaggle/input/advertising-sales-dataset/Advertising Budget and Sales.csv'))
asus.head(10) | code |
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