markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Now let's do the same thing with the decoy molecules | decoy_df = pd.read_csv("mk01/decoys_final.ism",header=None,sep=" ")
decoy_df.columns = ["SMILES","ID"]
decoy_rows, decoy_cols = decoy_df.shape
decoy_df["label"] = ["Decoy"]*decoy_rows
PandasTools.AddMoleculeColumnToFrame(decoy_df,"SMILES","Mol")
add_property_columns_to_df(decoy_df)
tmp_df = active_df.append(decoy_df) | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
With properties calculated for both the active and the decoy sets, we can compare the properties of the two compound sets. To do the comparison, we will use violin plots. A violin plot can be thought of as analogous to a boxplot. The violin plot provides a mirrored, horizontal view of a frequency distribution. Ideal... | sns.violinplot(tmp_df["label"],tmp_df["mw"]) | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
An examination of the distributions in the figures above show that the molecular weight distributions for the two setsare roughly equivalent. The decoy set has more low molecular weight molecules, but the center of the distribution, show as a box in the middle of each violin plot is in a similar location in both plots... | sns.violinplot(tmp_df["label"],tmp_df["logP"]) | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
Finally, we will do the same comparison with the formal charges of the molecules. | sns.violinplot(tmp_df["label"],tmp_df["charge"]) | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
In this case, we see a signficant difference. All of the active molecules are neutral, while some of the decoys are charged. Let see what fraction of the decoy molecules are charged. We can do this by creating a new dataframewith just the charged molecules. | charged = decoy_df[decoy_df["charge"] != 0] | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
A pandas dataframe has a property, shape, that returns the number of rows and columns in the dataframe. As such,element[0] in the shape property will be the number of rows. Let's divide the number of rows in our dataframe of charged molecules by the total number of rows in the decoy dataframe. | charged.shape[0]/decoy_df.shape[0] | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
The fact that 16% of the decoy compounds are charged, while none of the active compounds are is a concern. An examination of both sets indicate that charge states were assigned to the decoys, but not to the active molecules. In order to be consistent, we will use some code from the RDKit Cookbook to neutralize the mol... | from neutralize import NeutraliseCharges | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
Now we will create a new dataframe with the SMILES, ID, and label for the decoys. | revised_decoy_df = decoy_df[["SMILES","ID","label"]].copy() | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
With this new dataframe in hand, we can replace the SMILES with the SMILES for the neutral form of the molecule. TheNeutraliseCharges function returns two values. The first is the SMILES for the neutral form of the molecule and the second is a boolean variable indicating whether the molecule was changed. In the code ... | revised_decoy_df["SMILES"] = [NeutraliseCharges(x)[0] for x in revised_decoy_df["SMILES"]] | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
Once we've replaced the SMILES, we can add a molecule column to our new dataframe and calculated properties again. | PandasTools.AddMoleculeColumnToFrame(revised_decoy_df,"SMILES","Mol")
add_property_columns_to_df(revised_decoy_df) | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
We can now append the dataframe with the active molecules to the one with the revised, neutral decoys and calculateanother box plot. | new_tmp_df = active_df.append(revised_decoy_df)
sns.violinplot(new_tmp_df["label"],new_tmp_df["charge"]) | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
An examination of the plot about show that there are very few charged molecules in the decoy set. We can use the same technique we used above to create a dataframe with only the charged molecules. We can then use this dataframe to determine the number of charged molecules remaining in the set. | charged = revised_decoy_df[revised_decoy_df["charge"] != 0]
charged.shape[0]/revised_decoy_df.shape[0] | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
We have now reduced the fraction of charged compounds from 16% to 0.3%. We can now be confident that our active and decoy sets are reasonbly well balanced. In order to use these datasets with DeepChem we need to write the molecules out as a csv file consisting of SMILES, Name, and an integer value indicating whether... | active_df["is_active"] = [1] * active_df.shape[0]
revised_decoy_df["is_active"] = [0] * revised_decoy_df.shape[0]
combined_df = active_df.append(revised_decoy_df)[["SMILES","ID","is_active"]]
combined_df.head() | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
Our final step in this section is to save our new combined_df as a csv file. The index=False option causes Pandas to not include the row number in the first column. | combined_df.to_csv("dude_erk1_mk01.csv") | _____no_output_____ | MIT | Chapter11/chapter_11_01_preparing_to_build_a_predictive_models.ipynb | peastman/DeepLearningLifeSciences |
Load features | featurepath = r'Z:\3DHistoData\Grading\LBP_features_surface.xlsx'
gpath = r'Z:\3DHistoData\Grading\PTAgreiditjanaytteet.xls'
save = r'Z:\3DHistoData\Grading'
total = 1
surf = 2
deep = 5
cc = 6
deepcell = 7
deepECM = 8
ccECM = 9
ccVasc = 10
choice = surf
pipeline_load(featurepath, gpath, save, choice) | Max grade: 3, min grade: 0
(32, 36)
[-0.61126449 1.55888136 0.81372917 0.47258567 -1.55170516 -1.5287743
-0.89660894 2.45591342 -0.47300054 0.49480033]
(36, 10)
Confusion matrix
[[ 5 3 0 0]
[ 1 7 3 0]
[ 0 1 10 2]
[ 0 1 3 0]]
Mean squared error, Area under curve 1 and 2
0.36308781086036107 0.79464285... | MIT | training/notebooks/LBP features old.ipynb | MIPT-Oulu/3DHistoGrading |
Calculate LBP features from .dat mean and std images | impath = r'Z:\3DHistoData\SurfaceImages\Deep'
impath = r'V:\Tuomas\PTASurfaceImages'
dtype = 'dat'
dtype = 'mat'
savepath = r'Z:\3DHistoData\Grading\LBP'
save = r'Z:\3DHistoData\Grading'
pipeline_lbp(impath, savepath, save, dtype) | _____no_output_____ | MIT | training/notebooks/LBP features old.ipynb | MIPT-Oulu/3DHistoGrading |
Desafio 5Neste desafio, vamos praticar sobre redução de dimensionalidade com PCA e seleção de variáveis com RFE. Utilizaremos o _data set_ [Fifa 2019](https://www.kaggle.com/karangadiya/fifa19), contendo originalmente 89 variáveis de mais de 18 mil jogadores do _game_ FIFA 2019.> Obs.: Por favor, não modifique o nome ... | from math import sqrt
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as sct
import seaborn as sns
import statsmodels.api as sm
import statsmodels.stats as st
from sklearn.decomposition import PCA
from loguru import logger
from sklearn.linear_model import LinearRegression
fr... | _____no_output_____ | MIT | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience |
Inicia sua análise a partir daqui | # Sua análise começa aqui.
fifa.dropna(inplace= True)
| _____no_output_____ | MIT | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience |
Questão 1Qual fração da variância consegue ser explicada pelo primeiro componente principal de `fifa`? Responda como um único float (entre 0 e 1) arredondado para três casas decimais. | def q1():
# Retorne aqui o resultado da questão 1.
pca = PCA(n_components = 1)
project = pca.fit(fifa)
varianciaExplicada = project.explained_variance_ratio_[0]
return varianciaExplicada.round(3)
pass | _____no_output_____ | MIT | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience |
Questão 2Quantos componentes principais precisamos para explicar 95% da variância total? Responda como un único escalar inteiro. | def q2():
# Retorne aqui o resultado da questão 2.
pca095 = PCA(n_components= 0.95)
project = pca095.fit_transform(fifa)
numeroComponentesPrincipais = project.shape[1]
return numeroComponentesPrincipais
pass | _____no_output_____ | MIT | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience |
Questão 3Qual são as coordenadas (primeiro e segundo componentes principais) do ponto `x` abaixo? O vetor abaixo já está centralizado. Cuidado para __não__ centralizar o vetor novamente (por exemplo, invocando `PCA.transform()` nele). Responda como uma tupla de float arredondados para três casas decimais. | x = [0.87747123, -1.24990363, -1.3191255, -36.7341814,
-35.55091139, -37.29814417, -28.68671182, -30.90902583,
-42.37100061, -32.17082438, -28.86315326, -22.71193348,
-38.36945867, -20.61407566, -22.72696734, -25.50360703,
2.16339005, -27.96657305, -33.46004736, -5.08943224,
-30.21994603, ... | _____no_output_____ | MIT | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience |
Questão 4Realiza RFE com estimador de regressão linear para selecionar cinco variáveis, eliminando uma a uma. Quais são as variáveis selecionadas? Responda como uma lista de nomes de variáveis. | def q4():
# Retorne aqui o resultado da questão 4.
x = fifa.drop(columns="Overall")
y = fifa["Overall"]
rfe = RFE(estimator= LinearRegression(), n_features_to_select= 5)
rfe.fit(x,y)
indexFeatureSelect = rfe.get_support(indices=True)
featureSelect = list(x.columns[indexFeatureSelect])
... | _____no_output_____ | MIT | Modulo 6/main.ipynb | ronaldogomes96/Acelera-Dev-DataScience |
Tile Sliding Domain | import random
import heapq
random.seed(13)
StateDimension=3
# StateDimension=4
#InitialState = [1,2,3,4,5,6,0,7,8]
InitialState = "123456078"
#GoalState=[1,2,3,4,5,6,7,8,0]
GoalState = "123456780"
# GoalState = "123456789ABCDEF0"
Actions = lambda s: ['u', 'd', 'l', 'r']
Opposite=dict([('u','d'),('d','u'),('l','r'),('r... | 123
456
078
123
056
478
123
506
478
| Apache-2.0 | c164s22ch3.ipynb | everestso/Fall21Spring22 |
Random Walk | def RandomWalk(state, steps):
actionSequence = []
actionLast = None
for i in range(steps):
action = None
while action==None:
action = random.choice(Actions(state))
action = action if (LegalMove(state, action)
and action!= Opposite[actionLast]) else None
actionLast = action
s... | 203
156
478
['l', 'l', 'u', 'u', 'r']
['l', 'd', 'd', 'r', 'r']
123
456
780
| Apache-2.0 | c164s22ch3.ipynb | everestso/Fall21Spring22 |
Example 1 | Problems = [RandomWalk(GoalState, 5) for _ in range(10)]
for i, s in Problems:
print ('"', i, '" , "', ''.join(map(str, ReverseMoves(s))), '",',
ManhattanDistance(i, GoalState), sep='')
NewState = ApplyMoves("dldrr", "103526478")
print (NewState)
PrintState("123456780")
print()
PrintState("103526478")
print... | ManhattanDistance= 5
Out of Place= 5
| Apache-2.0 | c164s22ch3.ipynb | everestso/Fall21Spring22 |
Example 2 | Problems = [RandomWalk(GoalState, 100) for _ in range(20)]
for i, s in Problems:
print ('"', i, '", ', ''.join(map(str, ReverseMoves(s))), '",',
OutOfPlace(i, GoalState), " ", ManhattanDistance(i, GoalState), sep='')
InitialState = "281607543"
GoalState = "123456780"
print ("ManhattanDistance=", ManhattanD... | 100
123456780
| Apache-2.0 | c164s22ch3.ipynb | everestso/Fall21Spring22 |
Simple 15 Puzzle Test | GoalState = "123456789ABCDEF0"
s15a = Result(GoalState, "l")
s15b = Result(GoalState, "u")
PrintState(GoalState)
print()
PrintState(s15a)
print('')
PrintState(s15b) | 123
456
789
ABC
DEF
0
['1', '2', '3']
['4', '5', '6']
['7', '8', '9']
['A', 'B', 'C']
['D', 'E', 'F']
['0']
123
456
789
ABC
0EF
D
| Apache-2.0 | c164s22ch3.ipynb | everestso/Fall21Spring22 |
Discussion | StateDimension=3
s1 = "821357064"
sol1="ruurdllurrdlurddllurrdllurrd"
r = ApplyMoves(sol1, s1)
PrintState(r)
StateDimension=4
s1 = "13275AE069C4DF8B"
sol1="dluullddrrruuldrddlurdlluulurrrddd"
r = ApplyMoves(sol1, s1)
PrintState(r)
StateDimension=4
s="FAC42B061D89E537"
sol1="LLURDLDRDRURDLUULDDRUUULLDRDDRUULURDRDDLLLURU... | 51
153
------Solution 1-------
1234
5678
9ABC
DEF0
------Solution 2-------
1234
5678
9ABC
DEF0
| Apache-2.0 | c164s22ch3.ipynb | everestso/Fall21Spring22 |
Assignment Challenge Problems | StateDimension=4
s="71A92CE03DB4658F "
sol1="LLLDDRURURDLDRULLULURRRDDLUULDDRUULDLDDRURULLDRRRD".lower()
print(len(sol1))
r = ApplyMoves(sol1, s)
PrintState(r)
StateDimension=4
s="02348697DF5A1EBC"
sol1="RDDRDLULDRUURDLULDDRRURULLULDDRRRULDRD".lower()
print(len(sol1))
r = ApplyMoves(sol1, s)
PrintState(r)
StateDimens... | 58
1234
5678
9ABC
DEF0
| Apache-2.0 | c164s22ch3.ipynb | everestso/Fall21Spring22 |
Goals 1. Visualize deep learning network 2. Understand how the final layer would change when number of classes in dataset changes What do you do with a deep learning model in transfer learning - These are the steps already done by contributors in pytorch, keras and mxnet - You take a deep learning architec... | ! wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1jE-ckk0JbrdbJvIBaKMJWkTfbRDR2MaF' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1jE-c... | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
Imports | #Using keras backend
# When installed using pip
from monk.keras_prototype import prototype
# When installed manually (Uncomment the following)
#import os
#import sys
#sys.path.append("monk_v1/");
#sys.path.append("monk_v1/monk/");
#from monk.keras_prototype import prototype | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
Creating and managing experiments - Provide project name - Provide experiment name | gtf = prototype(verbose=1);
gtf.Prototype("Project", "study-num-classes"); | Keras Version: 2.2.5
Tensorflow Version: 1.12.0
Experiment Details
Project: Project
Experiment: study-num-classes
Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Organization/development/v5.0_blocks/study_roadmap/change_post_num_layers/5_transfer_learning_params/1_number_of_classes_in_datas... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
This creates files and directories as per the following structure workspace | |--------Project | |-----study-num-classes | |-----experiment-state.json ... | gtf.Default(dataset_path="study_classes/dogs_vs_cats",
model_name="resnet50",
num_epochs=5); | Dataset Details
Train path: study_classes/dogs_vs_cats
Val path: None
CSV train path: None
CSV val path: None
Dataset Params
Input Size: 224
Batch Size: 4
Data Shuffle: True
Processors: 4
Train-val split: 0.7
Found 36 images belonging to 2 classes.
Found 14 images ... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
From Data summary - Num classes: 2 Visualize network | gtf.Visualize_With_Netron(data_shape=(3, 224, 224), port=8081); | Using Netron To Visualize
Not compatible on kaggle
Compatible only for Jupyter Notebooks
Serving 'final.h5' at http://localhost:8081
| Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
The final layer | from IPython.display import Image
Image(filename='imgs/2_classes_base_keras.png') | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
Reset Default Params with new dataset - Logo classification | gtf = prototype(verbose=1);
gtf.Prototype("Project", "study-num-classes");
gtf.Default(dataset_path="study_classes/logos",
model_name="resnet50",
num_epochs=5); | Dataset Details
Train path: study_classes/logos
Val path: None
CSV train path: None
CSV val path: None
Dataset Params
Input Size: 224
Batch Size: 4
Data Shuffle: True
Processors: 4
Train-val split: 0.7
Found 82 images belonging to 16 classes.
Found 28 images belong... | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
From Data summary - Num classes: 16 Visualize network | gtf.Visualize_With_Netron(data_shape=(3, 224, 224), port=8082); | Using Netron To Visualize
Not compatible on kaggle
Compatible only for Jupyter Notebooks
Serving 'final.h5' at http://localhost:8082
| Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
The final layer | from IPython.display import Image
Image(filename='imgs/16_classes_base_keras.png') | _____no_output_____ | Apache-2.0 | study_roadmaps/2_transfer_learning_roadmap/3_effect_of_number_of_classes_in_dataset/3) Understand transfer learning and the role of number of dataset classes in it - Keras.ipynb | take2rohit/monk_v1 |
Baseline: | # Define simulation:
env = RingRoad(
num_vehicles = 22, # The vechicles at index 0 is an A.V.
ring_length = 230.0, # The road is a cicle.
starting_noise = 4.0, # Uniformly add noise to starting positions.
temporal_res = 0.3, # Set the size of simulation steps (seconds).
av_activate = 30, # Set ... | _____no_output_____ | MIT | notebooks/Animations.ipynb | chickert/autonomous_vehicles |
Extension 1 | # Define simulation:
num_vehicles = 22
num_avs = 11
env = RingRoad(
num_vehicles=num_vehicles, # The vechicles at index 0 is an A.V.
ring_length=230.0, # The road is a cicle.
starting_noise=4.0, # Uniformly add noise to starting positions.
temporal_res=0.3, # Set the size of simulation steps (second... | _____no_output_____ | MIT | notebooks/Animations.ipynb | chickert/autonomous_vehicles |
Extension 2 | # Define simulation:
a_sigma = 0.04
b_sigma = 0.5
env = RingRoad(
num_vehicles=22, # The vechicles at index 0 is an A.V.
ring_length=230.0, # The road is a cicle.
starting_noise=0., # Uniformly add noise to starting positions.
temporal_res=0.3, # Set the size of simulation steps (seconds).
av_ac... | _____no_output_____ | MIT | notebooks/Animations.ipynb | chickert/autonomous_vehicles |
Extension 3 | # Define simulation:
sigma_pct = 40
env = RingRoad(
num_vehicles=22, # The vechicles at index 0 is an A.V.
ring_length=230.0, # The road is a cicle.
starting_noise=4.0, # Uniformly add noise to starting positions.
temporal_res=0.3, # Set the size of simulation steps (seconds).
av_activate=30, #... | _____no_output_____ | MIT | notebooks/Animations.ipynb | chickert/autonomous_vehicles |
**Neural Networks Summary** | from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten
from keras.utils import to_categorical | _____no_output_____ | MIT | Intro/nn_summary.ipynb | temuller/kaggle_competitions |
Regression | model = Sequential()
n_cols = data.shape[1]
model.add(Dense(5, activation='relu', input_shape=(n_cols, ))) # input shape has to be the same as number of columns
model.add(Dense(5, activation='relu'))
model.add(Dense(1)) # output layer
model.compile(optimizer='adam', loss='mean_square_error')
# adam is more effcici... | _____no_output_____ | MIT | Intro/nn_summary.ipynb | temuller/kaggle_competitions |
Classification | model = Sequential()
n_cols = data.shape[1]
target = to_categorical(target)
model.add(Dense(5, activation='relu', input_shape=(n_cols, )))
model.add(Dropout(0.2)) # dropout is a regularization technique to prevent overfitting. Normally ~0.2-0.4
model.add(Dense(5, activation='relu'))
model.add(Dropout(0.2))
model.ad... | _____no_output_____ | MIT | Intro/nn_summary.ipynb | temuller/kaggle_competitions |
Convolutional Neural Networks (CNN) - supervisedThis are mainly use for images as they reduce dimensionality. Check this [link](https://courses.edx.org/courses/course-v1:IBM+DL0101EN+3T2019/courseware/89227024130b43f684d95376901b65c8/052a444d45914712a597f0c58cbc4391/?child=first) | model = Sequential()
input_shape = (N, N, 3) # 3 for RGB images and 1 for gray scale images
model.add(Conv2D(16, kernel_size=(2, 2), # size of the filter to use
strides=(1, 1), # steps the filter is moved
activation='relu',
input_shape=input_shape))
model.add(Ma... | _____no_output_____ | MIT | Intro/nn_summary.ipynb | temuller/kaggle_competitions |
String Data type | 'Hello'
"Hello"
'Hello' + ' world'
print('Hello World!')
a="kumaran"
a[3]
a[1:]
a[0:]
a[:]
a[:-1]
a[:-10]
a[::-1]
a[::-10]
a[::2]
a[::3]
a="kumaran"
a[::3]
a[2::1]
a.count
a.endswith
a.endswith('n')
a.endswith('m')
a.count('a')
a = "kumran systems"
a.title()
a.find('q')
a.find('a')
a="kumaran"
a.find('a')
a.isnumeric()... | kiumran
| MIT | Python Basic.ipynb | ramsvijay/basic_datatype_python |
List Object | a.isascii()
my_list = [1,2,"ram"]
my_list[2]
print(my_list)
my_list.index(1)
my_list.index('ram')
my_list.pop()
print(my_list)
my_list.clear()
print(my_list)
my_list.insert(10,'ram')
print(my_list)
my_list *4
a = [[1,2,3],[4,5,6],[7,8,9]]
[row[1] for row in a]
my_dict = {'key':'ram','value':['a',1]}
print(my_dict['valu... | _____no_output_____ | MIT | Python Basic.ipynb | ramsvijay/basic_datatype_python |
Tuple | t=(1,2,3,4,{'key':'value'})
t.index({'key':'value'}) | _____no_output_____ | MIT | Python Basic.ipynb | ramsvijay/basic_datatype_python |
Set | n={1.2,12,}
m={'rams'} | _____no_output_____ | MIT | Python Basic.ipynb | ramsvijay/basic_datatype_python |
m = n.copy | a=m
a.isdisjoint(12)
print(a) | {1.2, 12, 1}
| MIT | Python Basic.ipynb | ramsvijay/basic_datatype_python |
Boolean | True
not False | _____no_output_____ | MIT | Python Basic.ipynb | ramsvijay/basic_datatype_python |
測試資料 | # 表格資料title
data = [
{"route_id": "0001", "route_desc": "路線1", "num_of_people": 100,
"origin_amt": 1000, "act_amt": 600, "subsidy_amt": 400, "avg_subsidy_amt_by_people": 4},
{"route_id": "0002", "route_desc": "路線2", "num_of_people": 100,
"origin_amt": 1000, "act_amt": 600, "subsidy_amt": 400, "avg_s... | _____no_output_____ | MIT | 11_openpyxl/report_01.ipynb | bryson0083/python_note |
EXCEL輸出格式產生 | wb = Workbook()
ws = wb.active
# 設定print area
# https://openpyxl.readthedocs.io/en/stable/print_settings.html
ws.print_options.horizontalCentered = True
# ws.print_options.verticalCentered = True
ws.print_area = 'A1:G10'
# 設定輸出表格字體參數
table_title_ft = Font(name='標楷體', color='000000', size=14, bold=True)
table_ft = Font(... | _____no_output_____ | MIT | 11_openpyxl/report_01.ipynb | bryson0083/python_note |
Semantic Function Species (part 2) | from scripts.imports import *
out = Exporter(
paths['outdir'],
'semantics'
)
from IPython.display import HTML, display
df.columns | _____no_output_____ | CC-BY-4.0 | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations |
Miscellaneous Functions | df[df.funct_type == 'secondary'].function.value_counts()
funct2names = {
'purposive_ext':['purpext', 'Purposive Extent'],
'dist_posterior': ['distpost', 'Distance Posterior'],
'anterior_limitive': ['antlimit', 'Anterior Limitive'],
'dist_prospective': ['distprosp', 'Distance Prospective'],
'purposiv... | purposive_ext
showing 10 of 10
| CC-BY-4.0 | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations |
Pull Out Examples Purposive Extent | purpext_df = df[df.function == 'purposive_ext']
out.number(
purpext_df.shape[0],
'purpext_N'
)
antlim_df = df[df.function == 'anterior_limitive']
out.number(
antlim_df.shape[0],
'antlim_N'
) | _____no_output_____ | CC-BY-4.0 | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations |
Difficult Cases Compound Time Adverbials | compound_ct = df[df.funct_type == 'compound'].function.value_counts()
out.table(
compound_ct,
'compound_funct_ct',
caption='Sampled Compound Time Adverbial Frequencies',
)
comp_clusters = {
'begin-to-end': [
'begin_to_end',
'habitual + begin_to_end',
'begin_to_end_habitual',
... | _____no_output_____ | CC-BY-4.0 | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations |
Auto Export Examples for Compounds | for cluster, labels in comp_clusters.items():
display(HTML(f'<h2>{cluster.title()}</h2>'))
for label in labels:
print(label)
ex_df = df[df.function == label]
display(
ts.show(
ex_df
)
)
display(HTML('<hr>')) | _____no_output_____ | CC-BY-4.0 | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations |
Manually Extract Specific Cases Begin-to-end | b2edf = df[df.function.isin(comp_clusters['begin-to-end'])]
out.number(
b2edf.shape[0],
'begintoend_N'
) | _____no_output_____ | CC-BY-4.0 | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations |
Calendricals | caldf = df[df.function == 'simultaneous_calendar']
out.number(
caldf.shape[0],
'N_simul_calendar'
)
caldf.times_utf8.value_counts() | _____no_output_____ | CC-BY-4.0 | workflow/notebooks/analysis/sem_fspecies2.ipynb | CambridgeSemiticsLab/BH_time_collocations |
COMP4096 Business Intelligence Group Project COVID-19 Data Analysis and Prediction This part is written by Wong Tin Yau David (18207871). Datasets below are downloaded from https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv, which is provided by https://ourworldindata.org/. (An... | import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None # default='warn'
df = pd.read_csv('owid-covid-data_2021.csv')
df.head()
df.shape | _____no_output_____ | MIT | COMP4096 GP Project.ipynb | dtywong/covid19-data-analysis |
2. Select the data only from G20 Countries only | g20df = df[(df["location"]=='Australia')|(df["location"]=='Canada')|(df["location"]=='Saudi Arabia')|(df["location"]=='United States')|(df["location"]=='India')|(df["location"]=='Russia')|(df["location"]=='South Africa')|(df["location"]=='Turkey')|(df["location"]=='Argentina')|(df["location"]=='Brazil')|(df["location"]... | _____no_output_____ | MIT | COMP4096 GP Project.ipynb | dtywong/covid19-data-analysis |
3. Seach columns with mostly-missing values and drop columns with over 99% values | (g20df.isnull().sum()/g20df.shape[0]).sort_values(ascending=False)
droppable_features.append('weekly_icu_admissions_per_million')
droppable_features.append('weekly_icu_admissions')
droppable_features.append('weekly_hosp_admissions')
droppable_features.append('weekly_hosp_admissions_per_million')
| _____no_output_____ | MIT | COMP4096 GP Project.ipynb | dtywong/covid19-data-analysis |
4. Find Too Skewed Columns and Remove | pd.options.display.float_format = '{:,.4f}'.format
sk_df = pd.DataFrame([{'column': c, 'uniq': g20df[c].nunique(), 'skewness': g20df[c].value_counts(normalize=True).values[0] * 100} for c in g20df.columns])
sk_df = sk_df.sort_values('skewness', ascending=False)
sk_df | _____no_output_____ | MIT | COMP4096 GP Project.ipynb | dtywong/covid19-data-analysis |
5. Find columns that have more than 10% of missing values and filled with means | null_counts = g20df.isnull().sum()
null_counts = null_counts / g20df.shape[0]
null_counts[null_counts > 0.1]
g20df.drop(droppable_features, axis=1, inplace=True)
g20df.shape
g20df['icu_patients'].fillna((g20df['icu_patients'].mean()), inplace=True)
g20df['icu_patients_per_million'].fillna((g20df['icu_patients_per_mill... | _____no_output_____ | MIT | COMP4096 GP Project.ipynb | dtywong/covid19-data-analysis |
6. Find correlations between each attributes | cols = g20df.columns.tolist()
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
co_cols = cols[:10]
co_cols.append('new_vaccinations')
sns.heatmap(g20df[co_cols].corr(), cmap='RdBu_r', annot=True, center=0.0)
plt.title('Correlation between 1 ~ 10th columns')
plt.show()
corr_remove = []
... | _____no_output_____ | MIT | COMP4096 GP Project.ipynb | dtywong/covid19-data-analysis |
Analysis of evoked response using ICA and PCA reduction techniquesThis example computes PCA and ICA of evoked or epochs data. Then thePCA / ICA components, a.k.a. spatial filters, are used to transformthe channel data to new sources / virtual channels. The output isvisualized on the average of all the epochs. | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Asish Panda <asishrocks95@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter
from sklearn.decomposition import PCA, FastI... | _____no_output_____ | BSD-3-Clause | 0.16/_downloads/plot_decoding_unsupervised_spatial_filter.ipynb | drammock/mne-tools.github.io |
Transform data with PCA computed on the average ie evoked response | pca = UnsupervisedSpatialFilter(PCA(30), average=False)
pca_data = pca.fit_transform(X)
ev = mne.EvokedArray(np.mean(pca_data, axis=0),
mne.create_info(30, epochs.info['sfreq'],
ch_types='eeg'), tmin=tmin)
ev.plot(show=False, window_title="PCA", time_unit='s') | _____no_output_____ | BSD-3-Clause | 0.16/_downloads/plot_decoding_unsupervised_spatial_filter.ipynb | drammock/mne-tools.github.io |
Transform data with ICA computed on the raw epochs (no averaging) | ica = UnsupervisedSpatialFilter(FastICA(30), average=False)
ica_data = ica.fit_transform(X)
ev1 = mne.EvokedArray(np.mean(ica_data, axis=0),
mne.create_info(30, epochs.info['sfreq'],
ch_types='eeg'), tmin=tmin)
ev1.plot(show=False, window_title='ICA', time_uni... | _____no_output_____ | BSD-3-Clause | 0.16/_downloads/plot_decoding_unsupervised_spatial_filter.ipynb | drammock/mne-tools.github.io |
Pandas Group By **In this tutorial we are going to look at weather data from various cities and see how group by can be used to run some analytics.** | import pandas as pd
df = pd.read_csv("weather_by_cities.csv")
df | _____no_output_____ | CNRI-Python | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas |
For this dataset, get following answers, 1. What was the maximum temperature in each of these 3 cities? 2. What was the average windspeed in each of these 3 cities? | g = df.groupby("city")
g | _____no_output_____ | CNRI-Python | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas |
**DataFrameGroupBy object looks something like below,**  | for city, data in g:
print("city:",city)
print("\n")
print("data:",data) | city: mumbai
data: day city temperature windspeed event
4 1/1/2017 mumbai 90 5 Sunny
5 1/2/2017 mumbai 85 12 Fog
6 1/3/2017 mumbai 87 15 Fog
7 1/4/2017 mumbai 92 5 Rain
city: new york
data: day city... | CNRI-Python | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas |
**This is similar to SQL,****SELECT * from weather_data GROUP BY city** | g.get_group('mumbai')
g.max()
g.mean() | _____no_output_____ | CNRI-Python | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas |
**This method of splitting your dataset in smaller groups and then applying an operation (such as min or max) to get aggregate result is called Split-Apply-Combine. It is illustrated in a diagram below**  | g.min()
g.describe()
g.size()
g.count()
%matplotlib inline
g.plot() | _____no_output_____ | CNRI-Python | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas |
Group data using custom function: Let's say you want to group your data using custom function. Here the requirement is to create three groups Days when temperature was between 80 and 90 Days when it was between 50 and 60 Days when it was anything else For this you need to write custom grouping function and pas... | def grouper(df, idx, col):
if 80 <= df[col].loc[idx] <= 90:
return '80-90'
elif 50 <= df[col].loc[idx] <= 60:
return '50-60'
else:
return 'others'
g = df.groupby(lambda x: grouper(df, x, 'temperature'))
g
for key, d in g:
print("Group by Key: {}\n".format(key))
print(d) | Group by Key: 50-60
day city temperature windspeed event
9 1/2/2017 paris 50 13 Cloudy
10 1/3/2017 paris 54 8 Cloudy
Group by Key: 80-90
day city temperature windspeed event
4 1/1/2017 mumbai 90 5 Sunny
5 1/2/2017 mumbai ... | CNRI-Python | 8_Grouping and Aggregating/1_Groupby/1_Code_Basics/pandas_group_by.ipynb | sureshmecad/Pandas |
TSG108 - View the controller upgrade config map===============================================Description-----------When running a Big Data Cluster upgrade using `azdata bdc upgrade`:`azdata bdc upgrade --name --tag `It may fail with:> Upgrading cluster to version 15.0.4003.10029\_2>> NOTE: Cluster upgrade can take a ... | # Define `run` function for transient fault handling, suggestions on error, and scrolling updates on Windows
import sys
import os
import re
import platform
import shlex
import shutil
import datetime
from subprocess import Popen, PIPE
from IPython.display import Markdown
retry_hints = {} # Output in stderr known to be... | _____no_output_____ | MIT | Big-Data-Clusters/CU14/public/content/diagnose/tsg108-controller-failed-to-upgrade.ipynb | glienard/tigertoolbox |
Get the Kubernetes namespace for the big data clusterGet the namespace of the Big Data Cluster use the kubectl command lineinterface .**NOTE:**If there is more than one Big Data Cluster in the target Kubernetescluster, then either:- set \[0\] to the correct value for the big data cluster.- set the environment vari... | # Place Kubernetes namespace name for BDC into 'namespace' variable
if "AZDATA_NAMESPACE" in os.environ:
namespace = os.environ["AZDATA_NAMESPACE"]
else:
try:
namespace = run(f'kubectl get namespace --selector=MSSQL_CLUSTER -o jsonpath={{.items[0].metadata.name}}', return_output=True)
except:
... | _____no_output_____ | MIT | Big-Data-Clusters/CU14/public/content/diagnose/tsg108-controller-failed-to-upgrade.ipynb | glienard/tigertoolbox |
View the upgrade configmap | run(f'kubectl get configmap -n {namespace} controller-upgrade-configmap -o yaml')
print("Notebook execution is complete.") | _____no_output_____ | MIT | Big-Data-Clusters/CU14/public/content/diagnose/tsg108-controller-failed-to-upgrade.ipynb | glienard/tigertoolbox |
1. Считаем данные | data_train = pd.read_csv("./Data/x_train.csv", delimiter=';', header=None)
target_train = pd.read_csv("./Data/y_train.csv", delimiter=';', header=None)
data_train.head()
np.unique(target_train, return_counts=True)
y = np.array(target_train) | _____no_output_____ | MIT | Seminars/Seminar_3/Seminar.ipynb | JacksonGibsonESP/Sberbank-ML |
2. Посмотрим на RF на сырых данных | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
def get_accuracies(X, Y):
rf_clf = RandomForestClassifier(n_estimators=100)
print("Accuracy: {0}".format(cross_val_score(rf_clf, X, Y, scoring='accuracy', cv=5, n_jobs=-1)))
get_accuracies(data_train, y) | /anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
estimator.fit(X_train, y_train, **fit_params)
/anaconda3/lib/python3.6/site-pack... | MIT | Seminars/Seminar_3/Seminar.ipynb | JacksonGibsonESP/Sberbank-ML |
3. Feature processing | from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
X = data_train.copy()
def plot_importance(clf, X):
importances = clf.feature_importances_
std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)
indices = np.argsort(importances)[::-1]
pl... | /anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py:458: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
estimator.fit(X_train, y_train, **fit_params)
/anaconda3/lib/python3.6/site-pack... | MIT | Seminars/Seminar_3/Seminar.ipynb | JacksonGibsonESP/Sberbank-ML |
15天入门Python3CopyRight by 黑板客 转载请联系heibanke_at_aliyun.com **上节作业**八皇后 | %load day08/eight_queen.py
a = gen_n_queen(5)
printsolution(next(a)) | _____no_output_____ | MIT | Code/day09.ipynb | heibanke/learn_python_in_15days |
day09：谈对象—高富帅和白富美1. 面向对象编程2. **封装**, 属性和方法3. **继承**4. **多态** 与 重载5. 作业 面向对象编程面向对象和面向过程的区别：1. 面向过程编程 (OPP) 定义各种数据，定义各种函数，然后操作数据和函数输出结果，解决一类问题。面向过程适合解决特定的简单问题。2. 面向对象编程 (OOP) 最重要的是把用到的数据和方法抽象成为类。使用不同的类进行建模。达到代码模块化。适合解决多变，复杂，不断更新的问题。三大特性：封装，继承，多态。 | class A(object):
nums_A = 0
def __init__(self, data):
# 初始化
self.data = data
A.nums_A += 1
def methods(self):
self.data -= 1
a = A(3)
print(a.data, A.nums_A)
b = A(5)
print(b.data, A.nums_A)
a.methods()
c = A(6)
print(a.data, A.nums_A)
%%html
<style>
.rende... | _____no_output_____ | MIT | Code/day09.ipynb | heibanke/learn_python_in_15days |
| A | | :- ||- data| |+ methods()| 举个王者荣耀简化版的例子说明一下：你要开发一个游戏，游戏中每个玩家都是一个英雄。英雄有两种，比如亚瑟是近战英雄，后羿是远程英雄。每种英雄都有不同的属性。1. name2. defence3. attack4. life请模拟游戏里两个英雄互相PK的过程。看谁先game over。 | # %load day09/pk_opp_01.py
# 面向过程
def cal_damage(A, B):
"""
计算A对B造成的伤害
"""
return A['attack'] * (1-B['defence']/(B['defence']+400))
tank = {'name':'亚瑟1', 'defence':200, 'attack':80, 'life':600}
archer = {'name':'后羿1', 'defence':100, 'attack':200, 'life':300}
def game_test(A, B):
while A['life']>... | _____no_output_____ | MIT | Code/day09.ipynb | heibanke/learn_python_in_15days |
封装，属性和方法 |Hero| | :- ||- name - defence - attack - life||+ \__init\__ (name, defence, attack, life) + damage (enemy) + alive ()| |Game| | :- ||- A - B||+ \__init\__ (A, B) + start () + end ()| | # %load day09/pk_oop_01.py
class Hero(object):
def __init__(self, name, defence, attack, life):
self.name = name
self.defence = defence
self.attack = attack
self.life = life
def damage(self, enemy):
d = self.attack * (1-enemy.defence/(enemy.defence+400))
... | _____no_output_____ | MIT | Code/day09.ipynb | heibanke/learn_python_in_15days |
封装：对象的属性建议只能通过调用对象的方法来修改。只对对象的方法可见。 问题：当玩家很多时，生成亚瑟和后羿的属性只需要改变name，而其他属性是统一的。怎么更简化？ 继承子类继承父类的属性和方法。 | # %load day09/pk_oop_02.py
from day09.pk_oop_01 import Game
class Hero(object):
nums = 0
def __init__(self, name, defence, attack, life):
self.name = name
self.defence = defence
self.attack = attack
self.life = life
Hero.nums += 1
def damage(self, enemy):
... | _____no_output_____ | MIT | Code/day09.ipynb | heibanke/learn_python_in_15days |
多态和重载 多态，方法和子类动态绑定重载，多个同名的方法，参数不同。 让亚瑟的攻击带有吸血功能，每次将伤害的10%转化成自己的生命，看能否打败后羿。 | class YaSe(Hero):
def __init__(self, name):
Hero.__init__(self, name, 200, 80, 2000)
def damage(self, enemy, xixie = 0.1):
d = self.attack * (1-enemy.defence/(enemy.defence+400))
enemy.life -= d
self.life += d * xixie
print('%s 对 %s 造成 %.2f 伤害，%s 还剩 %.2f 生命。'%(self.name,... | _____no_output_____ | MIT | Code/day09.ipynb | heibanke/learn_python_in_15days |
Starting with Chollet's advice -- program simple programs that can solve the first 10 tasks | import numpy as np
import json
from PIL import Image, ImageDraw
from IPython.display import Image as Im
import matplotlib.pyplot as plt
import collections
colorMap = {0:"black",1:"blue",2:"red", 3:"green",4:"yellow",5:"grey",6:"magenta",7:"orange",8:"cyan",9:"brown"}
# accuracy of predicted output to actual output (si... | _____no_output_____ | Apache-2.0 | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC |
Functions for displaying grids | # displaying a single grid
def DisplayGrid(grid):
grid = np.asarray(grid)
nrows = len(grid[:,0])
ncols = len(grid[0,:])
height, width = nrows*50, ncols*50
image = Image.new(size=(width,height),mode='RGB',color=(255,255,255))
draw = ImageDraw.Draw(image)
r = 0
for row in grid:
c ... | _____no_output_____ | Apache-2.0 | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC |
Solving grid 1 | filename = "/Users/aysjajohnson/Desktop/ARC-master/data/training/007bbfb7.json"
with open(filename, 'r') as f:
grid = json.load(f)
inGrid = grid["train"][0]["input"]
outGrid = grid["train"][0]["output"]
def returnArrays(inputGrid, outputGrid):
return(np.asarray(inputGrid),np.asarray(outputGrid))
def inDim2o... | _____no_output_____ | Apache-2.0 | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC |
Solving Grid 2 | filename = "/Users/aysjajohnson/Desktop/ARC-master/data/training/00d62c1b.json"
with open(filename, 'r') as f:
grid = json.load(f)
inGrid = grid["train"][3]["input"]
outGrid = grid["train"][0]["output"]
DisplayGrids(inGrid,outGrid)
def bfs(grid, start, width, height, wall=3):
goal = 10
queue = collectio... | _____no_output_____ | Apache-2.0 | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC |
Solving Grid 3 | filename = "/Users/aysjajohnson/Desktop/ARC-master/data/training/017c7c7b.json"
with open(filename, 'r') as f:
grid = json.load(f)
inGrid = grid["train"][0]["input"]
outGrid = grid["train"][0]["output"]
DisplayGrids(inGrid,outGrid)
# making copy and paste more general
def patternCompletion(inputGrid, outputGrid... | _____no_output_____ | Apache-2.0 | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC |
Solving Grid 4 | filename = "/Users/aysjajohnson/Desktop/ARC-master/data/training/025d127b.json"
with open(filename, 'r') as f:
grid = json.load(f)
inGrid = grid["train"][0]["input"]
outGrid = grid["train"][0]["output"]
DisplayGrids(inGrid,outGrid)
def moveRight(grid):
grid = np.asarray(grid)
height = grid.shape[0]
... | _____no_output_____ | Apache-2.0 | notebooks/.ipynb_checkpoints/programs-checkpoint.ipynb | aysjajohnson/ARC |
Introdução ao Pandas - Viagens do Governo | Tratamento de dados *Esse notebook usa o arquivo sobre [viagens de funcionários do governo](http://www.portaltransparencia.gov.br/viagens) disponibilizado no portal da transparência.* | import pandas as pd
df_viagem = pd.read_csv('viagens_2019.csv', encoding='latin-1', sep=';') | _____no_output_____ | MIT | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy |
**Saber informações sobre uma coluna específica** | df_viagem['Valor passagens'] | _____no_output_____ | MIT | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy |
**Mudar as virgulas dos valores por pontos** | # o str é pra sinalizar que a troca do replace é uma string
df_viagem['Valor passagens'] = df_viagem['Valor passagens'].str.replace(',','.') | _____no_output_____ | MIT | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy |
**Mudar o tipo de dado da coluna** | df_viagem['Valor passagens'] = df_viagem['Valor passagens'].astype(float) | _____no_output_____ | MIT | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy |
**Contar quantas vezes aparece uma determinada coisa** | df_viagem['Destinos'].value_counts() | _____no_output_____ | MIT | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy |
**Separar as cidades dos Estados ou Países**O `split()` guarda o resultado em uma **lista** | # O parametro 'expand = True' indica que os elementos devem ser separados em diferentes colunas
col = df_viagem['Destinos'].str.split('/',1,expand = True) | _____no_output_____ | MIT | Tratamento de dados.ipynb | anicelysantos/jornalismo-de-dados-udemy |
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