• Creating a DataFrame
• Location
• type transforming
• Groupby and agg
• Example
  • Example 1

Creating a DataFrame

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import pandas as pd
df = pd.read_csv("a.csv")
df = pd.DataFrame({dict_t}) #convert a dict to a dataframe
df = Series_t.to_frame()

Location

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df.loc[0:4,"Year":"Party"]
df.loc[[1,2,5],["Year","Candidate"]]
df.loc[:,["Year","Candidate"]]
df.loc[:,"year"] # return series
df.loc[1,"year":"Party"] # return series
df.shape[0] # count of row
df[["Year","Candidate"]][0:5]

type transforming

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df['column_name'] = df['column_name'].astype(int)
df['column_name'] = df['column_name'].astype(float)
df['column_name'] = df['column_name'].astype(str)

Groupby and agg

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df.groupby('column_name').agg(['sum', 'mean', 'max', 'min'])
df.groupby(['column_name_1', 'column_name_2']).agg({'column_name_3': ['sum', 'mean'], 'column_name_4': ['max', 'min']})
df.groupby(['column_name_1', 'column_name_2']).agg(['sum', 'mean', 'max', 'min'])

Example

Example 1

Extracting the top 20 categories from a DataFrame using groupby:

1. Use the groupby and size methods to calculate the size of each group:

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   group_sizes = df.groupby('category').size() ```` 2. Use the `sort_values` method to sort the group sizes in descending order: ```python sorted_groups = group_sizes.sort_values(ascending=False)

2. Use the head method to extract the top 20 categories:

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top_20 = sorted_groups.head(20)

4. Use the isin method to filter the original DataFrame to only include rows with the top 20 categories:

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df_filtered = df[df['category'].isin(top_20.index)]

This will extract the top 20 categories from the ‘category’ column of the DataFrame and create a new DataFrame that only includes rows with those categories.

• Beautiful Soup
  • Generate bs4
  • find node
  • siblings
  • with Regular Expression

Beautiful Soup

Generate bs4

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soup = bs4.BeautifulSoup(text)

find node

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soup.find("div", attrs={"id":"..."})
soup.find_all("...")
soup.div

siblings

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soup.find("...").next_sibling
soup.find("...").previous_sibling
for elm in soup.div.next_siblings:
    ...

with Regular Expression

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soup(class_=re.compile('item-'))
soup(attrs={"class":re.compile("star-rating.*")})[0].get("class")[1]

Some basic operation

Shuffle the training set for hold out validation

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from sklearn.utils import shuffle
training_set,dev_set = np.split(shuffle(data_sample_35),[25])

Also capable of spliting training set, validation(development) set, test set

• Syntax
• Usage
  • Use re
  • Use Pandas
• Example
  • Example 1

Syntax

regex101
regexr

Usage

Use re

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re.sub(pattern,repl,str)
re.search(pattern, str)
re.findall(pattern,str)

Use Pandas

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df['column_name'].str.contains('pattern')
df['column_name'].str.findall('pattern')
df['column_name'].str.replace('pattern', 'replacement')

Example

Example 1

Extracting room numbers from a ‘Description’ column in a DataFrame using regular expressions:

1. Import the re module:

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   import re ```` 2. Define a function to extract the room number from a description: ```python def extract_room_number(description): match = re.search(r'(\d+\.\d+|\d+)(?=\s+of which are bedrooms)', description) if match: return float(match.group(1)) else: return None

2. Use the apply() method to apply the function to the ‘Description’ column and create a new ‘RoomNumber’ column:

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df['RoomNumber'] = df['Description'].apply(extract_room_number)

This will extract the room number from the ‘Description’ column and store it in the new ‘RoomNumber’ column.

Generator file

File Path: catkin_ws/src/test/script/env_pkl_generator.py

Launch Ros

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cd ~
roslaunch test demo_gazebo.launch

It will open Gazebo and RViz and display the robotic arm on both sofware.

Load Scene

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cd ~
rosrun test moveit_setscene.py
rosrun test gazebo_setscene.py

远程链接

向日葵
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• Environment
• Example

Environment

Install package for sql

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pip install sqlalchemy
pip install ibm_db_sa
pip install ipython-sql

Example

• Python related
  • Magma
  • Jupynium

Python related

Magma

magma-nvim
Magma is a NeoVim plugin for running code interactively with Jupyter.

Jupynium

Jupynium.nvim
It’s just like a markdown live preview, but it’s Jupyter Notebook live preview!

Jupynium uses Selenium to automate Jupyter Notebook, synchronising everything you type on Neovim.
Never leave Neovim. Switch tabs on the browser as you switch files on Neovim.