path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17123947/cell_3 | [
"text_plain_output_1.png"
] | !pip install pyspark | code |
17123947/cell_10 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
my_spark = SparkSession.builder.getOrCreate()
file_path = '../input/flights.csv'
flights = my_spark.read.csv(file_path, header=True)
flights.createOrReplaceTempView('flights')
flights = flights.withColumn('duration_hrs', flights.air_time / 60)... | code |
17123947/cell_12 | [
"text_plain_output_1.png"
] | !pip install pyspark_dist_explore
# https://github.com/Bergvca/pyspark_dist_explore/ | code |
17123947/cell_5 | [
"text_html_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
my_spark = SparkSession.builder.getOrCreate()
print(my_spark.catalog.listTables()) | code |
106210513/cell_42 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum() | code |
106210513/cell_25 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_34 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_23 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_30 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_33 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_20 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.head(10) | code |
106210513/cell_39 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_26 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
print(f'Data Contains {df.shape[0]} rows , {df.shape[1]} columns') | code |
106210513/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 |
106210513/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)
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.info() | code |
106210513/cell_18 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_32 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_28 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_8 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
plt.figure(figsize=(7, 8))
sns.countplot(df.dtypes)
plt.title('Count of DTypes of Dat... | code |
106210513/cell_15 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()] | code |
106210513/cell_38 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_35 | [
"text_plain_output_1.png",
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_43 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_31 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_24 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_14 | [
"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
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
print(f'duplicated rows = {df.duplicated().sum()}... | code |
106210513/cell_27 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106210513/cell_12 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T | code |
106210513/cell_36 | [
"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
df = pd.read_csv('../input/car-price-prediction-challenge/car_price_prediction.csv')
df.isnull().sum()
df.describe().T
df.loc[df.duplicated()]
df.drop_duplicates(inpla... | code |
106195366/cell_2 | [
"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 |
106195366/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
age_10 = pd.read_csv('/kaggle/input/korea-age-repartition-20102021/2010 - regional sex and age pop.csv', encoding='euc_kr')
age_11 = pd.read_csv('/kaggle/input/korea-age-repartition-20102021/2011 - regional sex and age pop.csv', encoding='euc_kr')
... | code |
129010475/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df2 = df.drop(['New_Price'], axis=1, inplace=False)
df.drop(['New_Price'], axis=1, inplace=True)
df.describe().T
df.info() | code |
129010475/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df['New_Price'].isnull().sum() | code |
129010475/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)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.head() | code |
129010475/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df2 = df.drop(['New_Price'], axis=1, inplace=False)
df.drop(['New_Price'], axis=1, inplace=True)
df.describe().T
unique_fuel = df['Fu... | code |
129010475/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df2 = df.drop(['New_Price'], axis=1, inplace=False)
df.drop(['New_Price'], axis=1, inplace=True)
df.describe().T | code |
129010475/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.head(2) | code |
129010475/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
len(df['Location'].unique()) | code |
129010475/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 |
129010475/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)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5) | code |
129010475/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df2 = df.drop(['New_Price'], axis=1, inplace=False)
df.drop(['New_Price'], axis=1, inplace=True)
df.head(1) | code |
129010475/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)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df2 = df.drop(['New_Price'], axis=1, inplace=False)
df2.head() | code |
129010475/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df2 = df.drop(['New_Price'], axis=1, inplace=False)
df.head(1) | code |
129010475/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
df2 = df.drop(['New_Price'], axis=1, inplace=False)
df.drop(['New_Price'], axis=1, inplace=True)
df.describe().T
df['Seats'].isnull()... | code |
129010475/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.sample(5)
unique_location = df['Location'].unique()
unique_location_list = unique_location.tolist()
len(unique_location_list) | code |
129010475/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/used-cars-price-prediction/train-data.csv')
df.head(-1) | code |
128039607/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airline)
flights = list(training_set.flight) + list(test_set.... | code |
128039607/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airli... | code |
128039607/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airli... | code |
128039607/cell_6 | [
"image_output_1.png"
] | import pandas as pd
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
training_set.head(5) | code |
128039607/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airli... | code |
128039607/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airli... | code |
128039607/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airline)
flights = list(training_set.flight) + list(test_set.... | code |
128039607/cell_8 | [
"image_output_1.png"
] | import pandas as pd
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
print('\n\nDatasetlarning qatorlar soni :\n', '#' * 40)
print('\nTraining Set : ', len(training_set))
print('Test S... | code |
128039607/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airline)
flights = list(training_set.flight) + list(test_set.... | code |
128039607/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
print(f"Raqamli ustunlar: \n {training_set.select_dtypes(['int', 'float']).columns} \n")
print(f"Harfli ustunlar: \... | code |
128039607/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
training_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/train_data.csv')
test_set = pd.read_csv('/kaggle/input/aviachipta-narxini-bashorat-qilish/test_data.csv')
airlines = list(training_set.airline) + list(test_set.airline)
flights = list(training_set.flight) + list(test_set.... | code |
32068026/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
meta = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
print('Cols names: {}'.format(meta.columns))
meta.head(7) | code |
32068026/cell_30 | [
"text_plain_output_1.png"
] | from gensim.parsing.preprocessing import remove_stopwords
from gensim.similarities import Similarity
from gensim.test.utils import datapath, get_tmpfile
from nltk.stem import LancasterStemmer
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize, sent_tokenize
import gensim
import pandas as... | code |
32068026/cell_28 | [
"text_plain_output_1.png"
] | from gensim.parsing.preprocessing import remove_stopwords
from gensim.similarities import Similarity
from gensim.test.utils import datapath, get_tmpfile
from nltk.stem import LancasterStemmer
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize, sent_tokenize
import gensim
import pandas as... | code |
32068026/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
meta = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
meta_dropped = meta.drop(['Microsoft Academic Paper ID', 'WHO #Covidence'], axis=1)
plt.figure(figsize=(20, 10))
meta_dropped.isna().sum().plot(kind='bar', stacked=True) | code |
32068026/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
meta = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
meta_dropped = meta.drop(['Microsoft Academic Paper ID', 'WHO #Covidence'], axis=1)
miss = meta['abstract'].isna().sum()
abstracts_papers = meta[meta['abstract'].notna()]
missing_doi = abstracts_papers['doi'].isna().sum(... | code |
32068026/cell_31 | [
"text_plain_output_1.png"
] | from gensim.parsing.preprocessing import remove_stopwords
from gensim.similarities import Similarity
from gensim.test.utils import datapath, get_tmpfile
from nltk.stem import LancasterStemmer
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize, sent_tokenize
import gensim
import pandas as... | code |
32068026/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
meta = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
meta_dropped = meta.drop(['Microsoft Academic Paper ID', 'WHO #Covidence'], axis=1)
miss = meta['abstract'].isna().sum()
print('The number of papers without abstracts is {:0.0f} which represents {:.2f}% of the total numbe... | code |
32068026/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
meta = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
meta_dropped = meta.drop(['Microsoft Academic Paper ID', 'WHO #Covidence'], axis=1)
miss = meta['abstract'].isna().sum()
abstracts_papers = meta[meta['abstract'].notna()]
print('The total number of papers is {:0.0f}'.for... | code |
32068026/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
meta = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
plt.figure(figsize=(20, 10))
meta.isna().sum().plot(kind='bar', stacked=True) | code |
106212426/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
df['bedrooms'].va... | code |
106212426/cell_25 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
print(lr.intercept_) | code |
106212426/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum() | code |
106212426/cell_26 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
tem... | code |
106212426/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape | code |
106212426/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes | code |
106212426/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
ax = plt.figure(f... | code |
106212426/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
tem... | code |
106212426/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
df['location'].nunique | code |
106212426/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
plt.scatter(df.be... | code |
106212426/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
plt.scatter(df.ba... | code |
106212426/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.head() | code |
106212426/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
df.plot.scatter('... | code |
106212426/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train) | code |
106212426/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
df['baths'].value... | code |
106212426/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
temp | code |
106212426/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.isnull().sum()
df.dtypes
temp = pd.DataFrame(index=df.columns)
temp['data_type'] = df.dtypes
temp['null_count'] = df.isnull().sum()
temp['unique_count'] = df.nunique()
plt.figure(figsiz... | code |
106212426/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/islamabad-house-prices/isb_data.csv')
df.shape
df.describe(include='all') | code |
74046533/cell_9 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True)
df.CRISPR_Cas.value_counts(ascending=True) | code |
74046533/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes | code |
74046533/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.plot() | code |
74046533/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.head() | code |
74046533/cell_11 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True)
df.CRISPR_Cas.value_counts(ascending=True)
df.corr() | code |
74046533/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv') | code |
74046533/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True) | code |
74046533/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True)
df.CRISPR_Cas.plot() | code |
74046533/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.describe() | code |
74046533/cell_10 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape
df.AMR.value_counts(ascending=True)
df.CRISPR_Cas.value_counts(ascending=True)
df.boxplot() | code |
74046533/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib as plt
import os
df = pd.read_csv('../input/amrc-data/Efaecium_AMRC.csv')
df.dtypes
df.shape | code |
2008232/cell_13 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').t... | code |
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