| heroku run pip freeze | grep tensorflow-tensorboard | xargs pip uninstall -y |
| <form method=post action="{{ url_for('make_prediction') }}" enctype=multipart/form-data> | |
| <label class="btn btn-default btn-file"> | |
| Browse<input type="file" name=file style="display: none;" accept="image/*" onchange="this.form.submit();"> | |
| </label> | |
| <button type="submit" style="display: none;"></button> | |
| </form> | |
| References | |
| - https://stackoverflow.com/questions/4730666/post-immediately-after-select-a-file |
| import tensorflow as tf | |
| above_threshold = (box_class_scores >= threshold) # shape: (19, 19, 5) | |
| scores = tf.boolean_mask(box_class_scores, above_threshold) |
Repository -> Repository Settings -> Remotes -> Edit Config File..
Put all your remotes in a single origin remote section, see below for example :
[remote "origin"]
url = https://myaccout@bitbucket.org/mynickname/myrepo
url = myuser@192.168.1.61:/srv/git/myrepo.git
fetch = +refs/heads/:refs/remotes/origin/
pushurl = https://myaccout@bitbucket.org/mynickname/myrepo
pushurl = myuser@192.168.1.61:/srv/git/myrepo.git
| def color_picker(x): | |
| if x == '東京': | |
| color_text = 'rgb(153, 204, 255)' | |
| else: | |
| color_text = 'rgb(153, 255, 51)' | |
| return 'background-color: {}'.format(color_text) | |
| df.style.applymap(color_picker, subset=pd.IndexSlice[:, ['col c']] |
| import seaborn as sns | |
| cm = sns.light_palette("green", as_cmap=True) | |
| df2.style.background_gradient(cmap=cm, subset=pd.IndexSlice[:, ['col1', 'col2']]) |
| df1['e'] = Series(np.random.randn(sLength), index=df1.index) | |
| References | |
| - https://stackoverflow.com/questions/12555323/adding-new-column-to-existing-dataframe-in-python-pandas |
| import pandas as pd | |
| df = pd.DataFrame() | |
| df.to_csv('YOUR_CSV_NAME', encoding='cp932') |
| import json | |
| import requests | |
| ES_URL = "your es cluster url including index and _type like .../index1/doc_type" | |
| query = { | |
| "size": 150, | |
| "query": { | |
| "bool": { | |
| "must": [ | |
| { |
| def get_phone_stat(group): | |
| return pd.DataFrame({ | |
| 'new_column': group['duration'].sum() | |
| }, index=[group.name]) | |
| df1 = df_phone.groupby('action').apply(get_phone_stat) | |
| df1.index = df1.index.droplevel(0) |
