Useful API

Both DataFrame and Series

API	Comments
`describe()`	Outputs useful statistical information about the data
`unique()`	Returns only unique rows/values in the data. TODO: validate on DF
`drop_duplicates()`	Removes all duplicate rows/values in the data
`isnull()`	Replaces all values that are NaN with True. False otherwise
`notnull()`	Replaces all values that are NaN with False. Tue otherwise
`dropna()`	Removes all rows that have any NaN value
`fillna(value)`	Fills all values that are NaN with the value given
`apply(fn)`	Applies the function to all values (TODO: Validate works on DF)
`index`	The name of the index
`nunique()`	Gets a count of the unique rows/values in the data
`copy()`	Makes a copy of the DF. Should be used judiciously
`take(iterable)`	Returns the rows found in the iterable TODO: Series?
`concat()`	Not the same as ‘append’. TODO: Series. Describe better.

Question: How to append multiple columns to a DataFrame?

DataFrame only

Available on DataFrame, Series and Groupby Result

Math methods

add, sub, mul, div, mod, pow

# do element-by-element math
result = df1.add(df2)

Series only

If you set a location to None (e.g. df.iloc[3, 2] = None), The value may become np.NaN if column is numeric
read_csv and parse_dates to get TimeSeries
Reading Unicode files: with (‘filename.txt, encoding=’utf-8’)
- This is helpful when copying content from Google Docs or Word
Series
- s.isin(list) # a mask
- (most of the DataFrame methods):
Iterating through a DataFrame
df[ X ] where X can be:
- ‘column_name’ - returns a Series
- Boolean Series - acts as a mask and returns a DataFrame
- ['col1', 'col2'] - using a list of column names returns a DataFrame
- Does a slice work? ['col1':'col2']
df.loc[ row, col ] where row/col can be:
- index value (or column name)
- list of values
- slice of values
- Will a boolean mask work?
API on DataFrame
- describe
- unique,
- index, columns
- copy
- to_csv:
- Math: add, sub, mul, div, mod, pow : elementwise math of two dataframes
- min, max, idxmin, idxmax : returns a Series with information for each column
- nunique:
- dropna:
- isnull and notnull
- nlargest(count, 'col')
- nsmallest(count, 'col')
- groupby
- sort_values('col')
- concat([df1, df2]) vs append which is deprecated.
- sort_index
- set_index and reset_index
- rename(dict) where dict is { 'orig': 'New_name' }
- apply
- plot
- boxplot
df = df.apply(pd.to_numeric, errors='coerce') : forces values to be numeric

import pandas as pd

# do details and examples of
# unique vs drop_duplicates (on df and series)
# groupby()
# .loc[]

more topics:

dropna or fillna
keep select columns with df = df[ col_list ]
perhaps coerce column dtypes: df = df.apply(pd.to_numeric, errors='coerce')
rename columns to something short and readable: df = df.rename(dict)

read_csv

For full documentation, see Pandas API reference

Formatting the Date:
In Pandas version 2.0, there is a date_format argument. But the version that NCHS has installed is older (v 1.3) and does not. In Jupyter Notebook, you can see your version using: print(pd._version).

Older versions support data_parser=fun. fun is a function to parse the date. It is recommended to use pd.to_datetime(date_string, format='%Y-%m-%d').

Here is an example that will parse MM-YYYY-DD formatted dates such as 03-2023-31.

df = pd.read_csv('data.csv', index_col='date', parse_dates=True, 
                 date_parser=lambda s: pd.to_datetime(s, format='%m-%Y-%d'))

When parse_dates=True, then we will parse the index as our date.

```{admonition} parse_dates :class: dropdown seealso

parse_dates: bool, list of Hashable, list of lists or dict of {Hashablelist}, default False

The line above has the following behavior:

bool. If True -> try parsing the index.
list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
list of list. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result foo

If a column or index cannot be represented as an array of datetime, say because of an unparsable value or a mixture of timezones, the column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use to_datetime() after read_csv().

## Iterating a DataFrame
There are several ways to iterate through a DataFrame.
```python
# iterates through the columns
for n in df2:
    print(n)

# iterates through the tuples (col_name, col_series)
for col_name, col_series in df2.iteritems():
    print(col_name)
    print(col_series)

# identical to the above
for col_name, col_series in df2.items():
    print(col_name)
    print(col_series)

DataFrame Plot API Summary

For full documentation, see Pandas DataFrame plot.

```{admonition} Documentation Notes :class: dropdown Note that in this documentation:

label means the column name in the DataFrame.
position is a integer representing the ordinal of the column.
```

x : label or position, default None (use the index as the x-axis)
y : label, position, list of labels or positions, default None.
kind : One of the following strings describing the kind of plot to produce.

‘line’ : line plot (default)
‘bar’ : vertical bar plot
‘barh’ : horizontal bar plot
‘hist’ : histogram
‘box’ : boxplot
‘kde’ : Kernel Density Estimation plot
‘density’ : same as ‘kde’
‘area’ : area plot
‘pie’ : pie plot
‘scatter’ : scatter plot (DataFrame only)
‘hexbin’ : hexbin plot (DataFrame only) ax : The axes object to plot to subplot : bool, True means to make separate subplots for each column.

There are many more arguments such as: title, grid, legend, style, xticks, yticks, xlim, ylim, xlabel, ylabel, rot, colormap, table, stacked

import pandas as pd
df = pd.DataFrame()
help(df.plot)