Week 6 glossary

Week 6 glossary#

Online resources#

The official pandas documentation page is at https://pandas.pydata.org/docs/reference/index.html.

The documentation of matplolib.dates submodule is at https://matplotlib.org/stable/api/dates_api.html

Internet download functions#

  • urlretrive() (from urllib.request): retrieve a file from the internet to the local file system.

  • gdown.download(): download a public Google Drive file to the local file system

  • zipfile.Zipfile(), <Zipfile>.extractall(): extract the content of a zip file.

Pandas functions#

  • pd.DataFrame(): create a new DataFrame manually

  • pd.read_csv(): reading a csv file into a DataFrame

  • pd.read_excel(): reading an Excel file into a DataFrame

  • pd.isna(): vectorized check on which values are missing

  • pd.to_datetime(): convert strings to datetime objects

  • pd.Series(): create a pandas Series from arrays or pandas indices

Pandas DataFrame attributes#

  • <DataFrame>.size: the size (number of entries) of the DataFrame

  • <DataFrame>.shape: the shape (number of rows, columns) of the DataFrame

  • <DataFrame>.ndim: the number of dimension of the DataFrame

  • <DataFrame>.columns: the column names of the DataFrame

  • <DataFrame>.index: the index (row labels) of the DataFrame

Pandas DataFrame methods#

DataFrame manipulation#

  • <DataFrame>.info(): print out summary information about the DataFrame

  • <DataFrame>.iloc[]: subset the DataFrame by row and column indices

  • <DataFrame>.loc[]: subset the DataFrame by row and column labels

  • <DataFrame>.dropna(): dropping rows with missing values from the DataFrame

  • <DataFrame>.sort_index(): sort the rows of a DataFrame by the values of its index

  • <DataFrame>.sort_values(): sort the rows of a DataFrame by values from certain columns

  • <DataFrame>.reset_index(): reset the index of a DataFrame as a regular column

  • <DataFrame>.set_index(): set a column of the DataFrame as the DataFrame’s row index

  • <DataFrame>.to_csv(): export a DataFrame as an external csv file

Descriptive statistics#

  • <DataFrame>.describe(): compute multiple descriptive statistics for all columns of a DataFrame all at once

  • <DataFrame>.mean(): compute the mean for all columns of a DataFrame

  • <DataFrame>.median(): compute the median for all columns of a DataFrame

  • <DataFrame>.quantile(): compute a given quantile for all columns of a DataFrame

  • <DataFrame>.var(): compute the variance for all columns of a DataFrame

  • <DataFrame>.std(): compute the standard deviation for all columns of a DataFrame

  • <DataFrame>.sem(): compute the standard error for all columns of a DataFrame

Pandas datetime Series attributes and methods#

  • <Series>.dt.strftime(): produce datetime strings from datetime Series

  • <Series>.dt.year: extract the years of the datetime Series

  • <Series>.dt.month: sxtract the months of the datetime Series

  • <Series>.dt.day: extract the days of the datetime Series

  • <Series>.dt.hour: extract the hours of the datetime Series

  • <Series>.dt.minute: extract the minutes of the datetime Series

  • <Series>.dt.dayofyear: extract the days of year of the datetime Series

Datetime plotting utilities from matplotlib.dates#

  • mdates.YearLocator(): place ticks on year-level intervals

  • mdates.MonthLocator(): place ticks on month-level intervals

  • mdates.DayLocator(): place ticks on day-level intervals

  • mdates.HourLocator(): place ticks on hour-level intervals

  • mdates.MinuteLocator(): place ticks on minute-level intervals

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