• Reading a data file with Pandas
  • Multiple formats: CSV is most popular
  • CSV options:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
    • Skip rows
    • Comment
    • Use specific columns
    • dtypes
    • na
• Exploring data
  • Viewing: head()/tail()/sample()
  • Size: len(), shape
  • Info: columns, info, dtypes
  • Structure: index, shape
• Selecting data: columns
• Descriptive statistics
  • mean, describe
• Quick plots

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https://www.epa.gov/enviroatlas/data-download-step-2

1. Exploring Data with Pandas:

• Basic form of a dataframe

  • Rows and columns (vs numpy array)
  • Values in a column all have the same data type
  • Can be seen as a list of lists or as a collection of dictionaries
  • Role of the index

• Loading data with Pandas

  • Basic format: read_csv
  • Overriding default data types
  • Skipping rows, comments
  • Only reading certain columns

• Viewing and inspecting data

  • Viewing formatted data with head

    tail

    sample

  • Dataframe properties: len, shape, columns, index, dtypes, info

• Selecting columns

  • Selecting 1 column → series
  • Selecting >1 column → dataframe

• Descriptive statistics

  • Stats on a series: mean, min, max, median, std, percentiles
  • Stats on a dataframe
  • Correlations with corr

• Basic Plots

  • Setting plot types
  • Axes
• Selecting data
• Filtering, sorting, & grouping data
• Data cleaning
• Joining dataframes

2. Processing Data with Pandas

• Basic calculations
• Filtering and updating data
• Dealing with missing data
• Data type conversions
• Unique values
• Sorting data
• Grouping and transforming data
• Joining data
• Writing data to a file