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import streamlit as st
import pandas as pd
from vega_datasets import data


"""
# Using different charting libraries
"""

@st.cache

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Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

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Sessions

New Session

• tmux new [-s name] [cmd] (:new) - new session

Switch Session

• tmux ls (:ls) - list sessions
• tmux switch [-t name] (:switch) - switches to an existing session

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tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

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How do you POST a JSON file with curl??

You can post a json file with curl like so:

curl -X POST -H "Content-Type: application/json" -d @FILENAME DESTINATION

so for example:

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import pandas as pd
import numpy as np

from pandas.api.types import is_numeric_dtype

np.random.seed(42)
age = np.random.randint(20,100,50)
name = ['name'+str(i) for i in range(50)]
address = ['address'+str(i) for i in range(50)]

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import pandas as pd
import numpy as np

from pandas.api.types import is_numeric_dtype

np.random.seed(42)
age = np.random.randint(20,100,50)
name = ['name'+str(i) for i in range(50)]
address = ['address'+str(i) for i in range(50)]

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import 'dart:async';
import 'dart:math';

/*
 * Findings:
 * streams can be single or broadcast, but not sure why the diff.
 * can check if a stream is broadcast with isBroadcast
 * StreamController make it easy to use a stream and send it events
 */

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import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix

#Start
train_data_path = 'F://data//Train'

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