Pandas astype multiple columns
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Make Python code look accessible to people who often say: “I have no idea why that works, but I’ll copy+edit it anyway if it does the job.” Demonstrate cool code you’ll want to break try Presentation Goals
Python Pandas top 25 Cheat Sheet by Ianh - Cheatography
columns 6. Select columns by data type drinks.se lec t_d typ es( inc lud e=' num ber') To select only the numeric columns drinks.se lec t_d typ es( inc lud e=[ 'nu mber', 'object', 'categ ory', 'datet ime']) To include multiple data types by passing a list
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MAKE NEW COLUMNS df.assign(Area=lambda df: df.Length*df.Height) Compute and append one or more new columns. gdf[‘Volume’] = gdf.Length*gdf.Height*gdf.Depth Add single column. pd.qcut(df.col, n, labels=False) Bin column into n buckets. pandas provides a large set of vector functions that operate on all columns of a
Data Dive 2: Loading and Summarizing Data
A good rule of thumb: if two numbers cannot be added together to produc e a logical result, they should be stored as strings. ' We can recast the zip and county ids as strings - with leading zeros - to make this dataframe easier to handle.
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Tidy data complements pandas’svectorized operations. pandas will automatically preserve observations as you manipulate variables. No other format works as intuitively with pandas. Reshaping Data –Change the layout of a data set M * A F M * A pd.melt(df) Gather columns into rows. df.pivot(columns='var', values='val') Spread rows into columns.
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Tidy data complements pandas’svectorized operations. pandas will automatically preserve observations as you manipulate variables. No other format works as intuitively with pandas. Reshaping Data –Change the layout of a data set M * A F M * A pd.melt(df) Gather columns into rows. df.pivot(columns='var', values='val') Spread rows into columns.
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training across multiple GPU instances, the run time is significantly shorter compared to conventional Pandas DataFrame processing with scikit-learn model training. This framework enables scaling up and out in the cloud as well as on-premises in a multinode, multi-GPU cluster. Next: Monitor Dask and RAPIDS with Prometheus and Grafana.
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The columns are made up of pandas Series objects (more below). ... df = df.astype(dtype) # type conversion DataFrame iteration methods df.iteritems()# (col-index, Series) pairs ... Note: can rename multiple columns at once. Selecting columns s = df['colName'] # select col to Series
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s.astype(float) - Converts the datatype of the series to float s.replace(1,'one') ... object values from multiple columns df.groupby(col1)[col2].mean() - Returns the ... Pandas KEY We’ll use shorthand in this cheat sheet df - A pandas DataFrame object s - A pandas Series object
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s.astype(float) - Convert the datatype of the series to float s.replace(1,'one') - Replace all values equal to ... object values from multiple columns df.groupby(col1)[col2].mean() - Return the ... Pandas KEY We’ll use shorthand in this cheat sheet df - A pandas DataFrame object
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