#pandastricks search results

Handling missing data like a boss! 💪 Dealing with NaNs or null values can be a headache, but Pandas provides robust tools like dropna() and fillna() to manage missing data gracefully. 🤯🔍 #DataCleaning #PandasTricks

🐍 Python Tip of the Day: Dealing with categorical data in Pandas? Use `pd.get_dummies()` to effortlessly convert them into indicator variables (one-hot encoding). This simplifies your data analysis! 🐱📈 #Pandas #GetDummies #PandasTricks #CategoricalData #InfiniteLoop

Save data from a website to CSV using BeautifulSoup. Thank me later #Pandas #PandasTricks #WebScraping #Python #Selenium #100DaysOfCode

Aggregate by multiple functions with #Pandas #PandasTricks #100daysofcode #Python

Google Sheet "CSV" into A Pandas Dataframe. Thank me later #Pandas #PandasTricks #Python #GoogleSheets #100daysofcode

Combine multiple CSV's into a single one. Thank me later! #Pandas #Python #PandasTricks #100DaysOfCode

Lesser-known pandas tricks (2019) towardsdatascience.com/5-lesser-known… #Lesser-known #PandasTricks

🐼🤹‍♂️ pandas trick #100! 🎉 Want to read a HUGE dataset into pandas but don't have enough memory? Randomly sample the dataset *during file reading* by passing a function to "skiprows" See example 👇 Thanks to @TedPetrou for this trick! 🙌 #Python #DataScience #pandastricks

🐼🤹‍♂️ pandas trick #94: Want to save a *massive* amount of memory? Fix your data types: ➡️ 'int8' for small integers ➡️ 'category' for strings with few unique values ➡️ 'Sparse' if most values are 0 or NaN More info: pythonspeed.com/articles/panda… by @itamarst #Python #pandastricks

🐼🤹‍♂️ pandas trick #83: Problem: Your dataset has many columns and you want to ensure the correct data types Solution: 1. Create CSV of column names & dtypes 2. Read it into a DF 3. Convert it to dict 4. Use dict to specify dtypes of dataset 👇 Example 👇 #Python #pandastricks

🐼🤹‍♂️ pandas trick #85: Three useful ways to convert one set of values to another: 1. map() using a dictionary 2. factorize() to encode each value as an integer 3. comparison statement to return boolean values See example 👇 #Python #DataScience #pandastricks @python_tip

🐼🤹‍♂️ pandas trick #89: Need to split names of variable length into first_name & last_name? 1. Use str.split(n=1) to split only once (returns a Series of lists) 2. Chain str[0] and str[1] on the end to select the list elements See example 👇 #Python #DataScience #pandastricks

🐼🤹‍♂️ pandas trick #98: Problem: Your DataFrame is in "wide format" (lots of columns), but you need it in "long format" (lots of rows) Solution: Use melt()! ⛄➡️💧 See example 👇 Long format is better for analysis, transformation, merges... #Python #DataScience #pandastricks

🐼🤹‍♂️ pandas trick #88: Goal: Rearrange the columns in your DataFrame Options: 1. Specify all column names in desired order 2. Specify columns to move, followed by remaining columns 3. Specify column positions in desired order See example 👇 #Python #pandastricks @python_tip

🐼🤹‍♂️ pandas trick #91: Need to create a time series dataset for testing? Use pd.util.testing.makeTimeDataFrame() Need more control over the columns & data? Generate data with np.random & overwrite index with makeDateIndex() See example 👇 #Python #DataScience #pandastricks

🐼🤹‍♂️ pandas trick #93: Want to combine the small categories in a Series (<10% frequency) into a single category? 1. Save the normalized value counts 2. Filter by frequency & save the index 3. Replace small categories with "Other" See example 👇 #Python #pandas #pandastricks

🐼🤹‍♂️ pandas trick #84: My favorite feature in pandas 0.25: If DataFrame has more than 60 rows, only show 10 rows (saves your screen space!) You can modify this: pd.set_option('min_rows', 4) See example 👇 More info: pandas.pydata.org/pandas-docs/st… #Python #DataScience #pandastricks

🐍 Python Tip of the Day: Dealing with categorical data in Pandas? Use `pd.get_dummies()` to effortlessly convert them into indicator variables (one-hot encoding). This simplifies your data analysis! 🐱📈 #Pandas #GetDummies #PandasTricks #CategoricalData #InfiniteLoop

~Something new~ Tried "Pandas Profiling" following @justmarkham's #pandastricks in a Jupyter notebook provisioned on @IBMWatson studio and here's the output...

Step 4: Outliers skewing your data? Visualize with boxplots or histograms, then tame them with winsorizing or scaling. Visualize, understand, act! #DataVisualization #PandasTricks [gpt-4]

Google Sheet "CSV" into A Pandas Dataframe. Thank me later #Pandas #PandasTricks #Python #GoogleSheets #100daysofcode

Aggregate by multiple functions with #Pandas #PandasTricks #100daysofcode #Python

🙏🏻 #python #pandas community for finally introducing 💥 in version 25 #pandastricks

🐼🤹‍♂️ pandas trick #97: Want to convert "year" and "day of year" into a single datetime column? 📆 1. Combine them into one number 2. Convert to datetime and specify its format See example 👇 List of all format codes: strftime.org #Python #DataScience #pandastricks

Combine multiple CSV's into a single one. Thank me later! #Pandas #Python #PandasTricks #100DaysOfCode

Save data from a website to CSV using BeautifulSoup. Thank me later #Pandas #PandasTricks #WebScraping #Python #Selenium #100DaysOfCode

A simple way to group data over time, by weekly, monthly, yearly by setting the frequency and doing calculations. #datascience #python #pandastricks