The challenges with all of the free Pandas videos and tutorials online are these:
1. Presenter quality varies
2. Sound/video quality varies
3. Teacher/presenter skills vary
4. Organization is often lacking
5. The resource (audio/video file) label usually doesn't describe the contents adequately, so much time is spent just looking around to find out if it's useful
6. Difficult to find again
7. Difficult to keep organized - and remember one's place - when navigating away from it and returning later
Currently, my go-to resources are:
1. Working through examples from the official found at PyData: http://pandas.pydata.org/
2. Taking the inexpensive, so-far high-quality Python courses at Udemy.
Notes:
I am starting to get the hang of various methods and functions. Tonight, I am studying these items:
1. Object Creation:
s = pd.Series([1, 3, 5, np.nan, 6, 8])
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
2. Viewing Data:
df.head()
df.tail(3)
df.index()
df.columns()
df.values()
df.describe()
df.T
df.sort_index(axis=1, ascending=False)
3. Data Selection:
df['A'] ## selecting a single column, which yields a series equivalent
df[0:3] ## slice rows
df.loc[dates[0]] ## get a cross-section using a label
df.loc[:, ['A','B']] ## select on a multi-access label
df.loc[dates[0], 'A'] ## for getting a scalar value
4. Selection by Position
df.iloc[3] ## via position of passed integers
df.iloc[3:5, 0:2] ## by integer slice
df.iloc[[1,2,4],[0,2] ## by lists of integer position locations, similar to numpy
df.iloc[1:3, :] ## for slicing rows explicitly
df.iloc[:, 1:3] ## for slicing columns explicitly
df.iloc[1,1] ## for getting a value explicitly
1. Presenter quality varies
2. Sound/video quality varies
3. Teacher/presenter skills vary
4. Organization is often lacking
5. The resource (audio/video file) label usually doesn't describe the contents adequately, so much time is spent just looking around to find out if it's useful
6. Difficult to find again
7. Difficult to keep organized - and remember one's place - when navigating away from it and returning later
Currently, my go-to resources are:
1. Working through examples from the official found at PyData: http://pandas.pydata.org/
2. Taking the inexpensive, so-far high-quality Python courses at Udemy.
Notes:
I am starting to get the hang of various methods and functions. Tonight, I am studying these items:
1. Object Creation:
s = pd.Series([1, 3, 5, np.nan, 6, 8])
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
2. Viewing Data:
df.head()
df.tail(3)
df.index()
df.columns()
df.values()
df.describe()
df.T
df.sort_index(axis=1, ascending=False)
3. Data Selection:
df['A'] ## selecting a single column, which yields a series equivalent
df[0:3] ## slice rows
df.loc[dates[0]] ## get a cross-section using a label
df.loc[:, ['A','B']] ## select on a multi-access label
df.loc[dates[0], 'A'] ## for getting a scalar value
4. Selection by Position
df.iloc[3] ## via position of passed integers
df.iloc[3:5, 0:2] ## by integer slice
df.iloc[[1,2,4],[0,2] ## by lists of integer position locations, similar to numpy
df.iloc[1:3, :] ## for slicing rows explicitly
df.iloc[:, 1:3] ## for slicing columns explicitly
df.iloc[1,1] ## for getting a value explicitly
