# Lesson 1¶

Create Data - We begin by creating our own data set for analysis. This prevents the end user reading this tutorial from having to download any files to replicate the results below. We will export this data set to a text file so that you can get some experience pulling data from a text file.
Get Data - We will learn how to read in the text file. The data consist of baby names and the number of babies born in the year 1880.
Prepare Data - Here we will simply take a look at the data and make sure it is clean. By clean I mean we will take a look inside the contents of the text file and look for any anomalities. These can include missing data, inconsistencies in the data, or any other data that seems out of place. If any are found we will then have to make decisions on what to do with these records.
Analyze Data - We will simply find the most popular name in a specific year.
Present Data - Through tabular data and a graph, clearly show the end user what is the most popular name in a specific year.

The pandas library is used for all the data analysis excluding a small piece of the data presentation section. The matplotlib library will only be needed for the data presentation section. Importing the libraries is the first step we will take in the lesson.

In [1]:
# Import all libraries needed for the tutorial

# General syntax to import specific functions in a library:
##from (library) import (specific library function)

# General syntax to import a library but no functions:
##import (library) as (give the library a nickname/alias)
import matplotlib.pyplot as plt
import pandas as pd #this is how I usually import pandas
import sys #only needed to determine Python version number
import matplotlib #only needed to determine Matplotlib version number

# Enable inline plotting
%matplotlib inline

In [2]:
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
print('Matplotlib version ' + matplotlib.__version__)

Python version 3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]
Pandas version 1.0.5
Matplotlib version 3.2.2


# Create Data¶

The data set will consist of 5 baby names and the number of births recorded for that year (1880).

In [3]:
# The initial set of baby names and birth rates
names = ['Bob','Jessica','Mary','John','Mel']
births = [968, 155, 77, 578, 973]


To merge these two lists together we will use the zip function.

In [4]:
zip?

Init signature: zip(self, /, *args, **kwargs)
Docstring:
zip(iter1 [,iter2 [...]]) --> zip object

Return a zip object whose .__next__() method returns a tuple where
the i-th element comes from the i-th iterable argument.  The .__next__()
method continues until the shortest iterable in the argument sequence
is exhausted and then it raises StopIteration.
Type:           type
Subclasses:

In [5]:
BabyDataSet = list(zip(names,births))
BabyDataSet

Out[5]:
[('Bob', 968), ('Jessica', 155), ('Mary', 77), ('John', 578), ('Mel', 973)]

We are basically done creating the data set. We now will use the pandas library to export this data set into a csv file.

df will be a DataFrame object. You can think of this object holding the contents of the BabyDataSet in a format similar to a sql table or an excel spreadsheet. Lets take a look below at the contents inside df.

In [6]:
df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])
df

Out[6]:
Names Births
0 Bob 968
1 Jessica 155
2 Mary 77
3 John 578
4 Mel 973

Export the dataframe to a csv file. We can name the file births1880.csv. The function to_csv will be used to export the file. The file will be saved in the same location of the notebook unless specified otherwise.

In [7]:
df.to_csv?

Signature:
df.to_csv(
path_or_buf: Union[str, pathlib.Path, IO[~AnyStr], NoneType] = None,
sep: str = ',',
na_rep: str = '',
float_format: Union[str, NoneType] = None,
columns: Union[Sequence[Union[Hashable, NoneType]], NoneType] = None,
index: bool = True,
index_label: Union[bool, str, Sequence[Union[Hashable, NoneType]], NoneType] = None,
mode: str = 'w',
encoding: Union[str, NoneType] = None,
compression: Union[str, Mapping[str, str], NoneType] = 'infer',
quoting: Union[int, NoneType] = None,
quotechar: str = '"',
line_terminator: Union[str, NoneType] = None,
chunksize: Union[int, NoneType] = None,
date_format: Union[str, NoneType] = None,
doublequote: bool = True,
escapechar: Union[str, NoneType] = None,
decimal: Union[str, NoneType] = '.',
) -> Union[str, NoneType]
Docstring:
Write object to a comma-separated values (csv) file.

.. versionchanged:: 0.24.0
The order of arguments for Series was changed.

Parameters
----------
path_or_buf : str or file handle, default None
File path or object, if None is provided the result is returned as
a string.  If a file object is passed it should be opened with
newline='', disabling universal newlines.

.. versionchanged:: 0.24.0

Was previously named "path" for Series.

sep : str, default ','
String of length 1. Field delimiter for the output file.
na_rep : str, default ''
Missing data representation.
float_format : str, default None
Format string for floating point numbers.
columns : sequence, optional
Columns to write.
header : bool or list of str, default True
Write out the column names. If a list of strings is given it is
assumed to be aliases for the column names.

.. versionchanged:: 0.24.0

Previously defaulted to False for Series.

index : bool, default True
Write row names (index).
index_label : str or sequence, or False, default None
Column label for index column(s) if desired. If None is given, and
header and index are True, then the index names are used. A
sequence should be given if the object uses MultiIndex. If
False do not print fields for index names. Use index_label=False
for easier importing in R.
mode : str
Python write mode, default 'w'.
encoding : str, optional
A string representing the encoding to use in the output file,
defaults to 'utf-8'.
compression : str or dict, default 'infer'
If str, represents compression mode. If dict, value at 'method' is
the compression mode. Compression mode may be any of the following
possible values: {'infer', 'gzip', 'bz2', 'zip', 'xz', None}. If
compression mode is 'infer' and path_or_buf is path-like, then
detect compression mode from the following extensions: '.gz',
'.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given
and mode is 'zip' or inferred as 'zip', other entries passed as

.. versionchanged:: 1.0.0

May now be a dict with key 'method' as compression mode
and other entries as additional compression options if
compression mode is 'zip'.

quoting : optional constant from csv module
Defaults to csv.QUOTE_MINIMAL. If you have set a float_format
then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
will treat them as non-numeric.
quotechar : str, default '\"'
String of length 1. Character used to quote fields.
line_terminator : str, optional
The newline character or character sequence to use in the output
file. Defaults to os.linesep, which depends on the OS in which
this method is called ('\n' for linux, '\r\n' for Windows, i.e.).

.. versionchanged:: 0.24.0
chunksize : int or None
Rows to write at a time.
date_format : str, default None
Format string for datetime objects.
doublequote : bool, default True
Control quoting of quotechar inside a field.
escapechar : str, default None
String of length 1. Character used to escape sep and quotechar
when appropriate.
decimal : str, default '.'
Character recognized as decimal separator. E.g. use ',' for
European data.

Returns
-------
None or str
If path_or_buf is None, returns the resulting csv format as a
string. Otherwise returns None.

--------
to_excel : Write DataFrame to an Excel file.

Examples
--------
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv(index=False)

Create 'out.zip' containing 'out.csv'

>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  # doctest: +SKIP
File:      c:\users\17862\anaconda3\lib\site-packages\pandas\core\generic.py
Type:      method


The only parameters we will use is index and header. Setting these parameters to False will prevent the index and header names from being exported. Change the values of these parameters to get a better understanding of their use.

In [8]:
df.to_csv('births1880.csv',index=False,header=False)


## Get Data¶

To pull in the csv file, we will use the pandas function read_csv. Let us take a look at this function and what inputs it takes.

In [9]:
read_csv?

Signature:
filepath_or_buffer: Union[str, pathlib.Path, IO[~AnyStr]],
sep=',',
delimiter=None,
names=None,
index_col=None,
usecols=None,
squeeze=False,
prefix=None,
mangle_dupe_cols=True,
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
iterator=False,
chunksize=None,
compression='infer',
thousands=None,
decimal: str = '.',
lineterminator=None,
quotechar='"',
quoting=0,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
delim_whitespace=False,
low_memory=True,
memory_map=False,
float_precision=None,
)
Docstring:
Read a comma-separated values (csv) file into DataFrame.

Also supports optionally iterating or breaking of the file
into chunks.

Additional help can be found in the online docs for
IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>_.

Parameters
----------
filepath_or_buffer : str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: file://localhost/path/to/table.csv.

If you want to pass in a path object, pandas accepts any os.PathLike.

By file-like object, we refer to objects with a read() method, such as
a file handler (e.g. via builtin open function) or StringIO.
sep : str, default ','
Delimiter to use. If sep is None, the C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator by Python's builtin sniffer
tool, csv.Sniffer. In addition, separators longer than 1 character and
different from '\s+' will be interpreted as regular expressions and
will also force the use of the Python parsing engine. Note that regex
delimiters are prone to ignoring quoted data. Regex example: '\r\t'.
delimiter : str, default None
Alias for sep.
header : int, list of int, default 'infer'
Row number(s) to use as the column names, and the start of the
data.  Default behavior is to infer the column names: if no names
are passed the behavior is identical to header=0 and column
names are inferred from the first line of the file, if column
names are passed explicitly then the behavior is identical to
header=None. Explicitly pass header=0 to be able to
replace existing names. The header can be a list of integers that
specify row locations for a multi-index on the columns
e.g. [0,1,3]. Intervening rows that are not specified will be
skipped (e.g. 2 in this example is skipped). Note that this
parameter ignores commented lines and empty lines if
skip_blank_lines=True, so header=0 denotes the first line of
data rather than the first line of the file.
names : array-like, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass header=0 to override the column names.
Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, default None
Column(s) to use as the row labels of the DataFrame, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.

Note: index_col=False can be used to force pandas to *not* use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.
usecols : list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in names or
inferred from the document header row(s). For example, a valid list-like
usecols parameter would be [0, 1, 2] or ['foo', 'bar', 'baz'].
Element order is ignored, so usecols=[0, 1] is the same as [1, 0].
To instantiate a DataFrame from data with element order preserved use
pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']] for columns
in ['foo', 'bar'] order or
pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]
for ['bar', 'foo'] order.

If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to True. An
example of a valid callable argument would be lambda x: x.upper() in
['AAA', 'BBB', 'DDD']. Using this parameter results in much faster
parsing time and lower memory usage.
squeeze : bool, default False
If the parsed data only contains one column then return a Series.
prefix : str, optional
Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
'X'...'X'. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.
dtype : Type name or dict of column -> type, optional
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,
'c': 'Int64'}
Use str or object together with suitable na_values settings
to preserve and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
engine : {'c', 'python'}, optional
Parser engine to use. The C engine is faster while the python engine is
currently more feature-complete.
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can either
be integers or column labels.
true_values : list, optional
Values to consider as True.
false_values : list, optional
Values to consider as False.
skipinitialspace : bool, default False
Skip spaces after delimiter.
skiprows : list-like, int or callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (int)
at the start of the file.

If callable, the callable function will be evaluated against the row
indices, returning True if the row should be skipped and False otherwise.
An example of a valid callable argument would be lambda x: x in [0, 2].
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with engine='c').
nrows : int, optional
Number of rows of file to read. Useful for reading pieces of large files.
na_values : scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values.  By default the following values are interpreted as
NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
'1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',
'nan', 'null'.
keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data.
Depending on whether na_values is passed in, the behavior is as follows:

* If keep_default_na is True, and na_values are specified, na_values
is appended to the default NaN values used for parsing.
* If keep_default_na is True, and na_values are not specified, only
the default NaN values are used for parsing.
* If keep_default_na is False, and na_values are specified, only
the NaN values specified na_values are used for parsing.
* If keep_default_na is False, and na_values are not specified, no
strings will be parsed as NaN.

Note that if na_filter is passed in as False, the keep_default_na and
na_values parameters will be ignored.
na_filter : bool, default True
Detect missing value markers (empty strings and the value of na_values). In
data without any NAs, passing na_filter=False can improve the performance
verbose : bool, default False
Indicate number of NA values placed in non-numeric columns.
skip_blank_lines : bool, default True
If True, skip over blank lines rather than interpreting as NaN values.
parse_dates : bool or list of int or names or list of lists or dict, default False
The behavior is as follows:

* boolean. 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 lists. 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 datetimes,
say because of an unparseable 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 pd.to_datetime after
pd.read_csv. To parse an index or column with a mixture of timezones,
specify date_parser to be a partially-applied
:func:pandas.to_datetime with utc=True. See
:ref:io.csv.mixed_timezones for more.

Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
If True and parse_dates is enabled, pandas will attempt to infer the
format of the datetime strings in the columns, and if it can be inferred,
switch to a faster method of parsing them. In some cases this can increase
the parsing speed by 5-10x.
keep_date_col : bool, default False
If True and parse_dates specifies combining multiple columns then
keep the original columns.
date_parser : function, optional
Function to use for converting a sequence of string columns to an array of
datetime instances. The default uses dateutil.parser.parser to do the
conversion. Pandas will try to call date_parser in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by parse_dates) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by parse_dates into a single array
and pass that; and 3) call date_parser once for each row using one or
more strings (corresponding to the columns defined by parse_dates) as
arguments.
dayfirst : bool, default False
DD/MM format dates, international and European format.
cache_dates : bool, default True
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.

iterator : bool, default False
Return TextFileReader object for iteration or getting chunks with
get_chunk().
chunksize : int, optional
See the IO Tools docs
<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>_
for more information on iterator and chunksize.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer' and
filepath_or_buffer is path-like, then detect compression from the
following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
decompression). If using 'zip', the ZIP file must contain only one data
file to be read in. Set to None for no decompression.
thousands : str, optional
Thousands separator.
decimal : str, default '.'
Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), optional
Character to break file into lines. Only valid with C parser.
quotechar : str (length 1), optional
The character used to denote the start and end of a quoted item. Quoted
items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default 0
Control field quoting behavior per csv.QUOTE_* constants. Use one of
QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
doublequote : bool, default True
When quotechar is specified and quoting is not QUOTE_NONE, indicate
whether or not to interpret two consecutive quotechar elements INSIDE a
field as a single quotechar element.
escapechar : str (length 1), optional
One-character string used to escape other characters.
comment : str, optional
Indicates remainder of line should not be parsed. If found at the beginning
of a line, the line will be ignored altogether. This parameter must be a
single character. Like empty lines (as long as skip_blank_lines=True),
fully commented lines are ignored by the parameter header but not by
skiprows. For example, if comment='#', parsing
#empty\na,b,c\n1,2,3 with header=0 will result in 'a,b,c' being
encoding : str, optional
Encoding to use for UTF when reading/writing (ex. 'utf-8'). List of Python
standard encodings
<https://docs.python.org/3/library/codecs.html#standard-encodings>_ .
dialect : str or csv.Dialect, optional
If provided, this parameter will override values (default or not) for the
following parameters: delimiter, doublequote, escapechar,
skipinitialspace, quotechar, and quoting. If it is necessary to
override values, a ParserWarning will be issued. See csv.Dialect
documentation for more details.
Lines with too many fields (e.g. a csv line with too many commas) will by
default cause an exception to be raised, and no DataFrame will be returned.
If False, then these "bad lines" will dropped from the DataFrame that is
returned.
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
delim_whitespace : bool, default False
Specifies whether or not whitespace (e.g. ' ' or '    ') will be
used as the sep. Equivalent to setting sep='\s+'. If this option
is set to True, nothing should be passed in for the delimiter
parameter.
low_memory : bool, default True
Internally process the file in chunks, resulting in lower memory use
while parsing, but possibly mixed type inference.  To ensure no mixed
types either set False, or specify the type with the dtype parameter.
Note that the entire file is read into a single DataFrame regardless,
use the chunksize or iterator parameter to return the data in chunks.
(Only valid with C parser).
memory_map : bool, default False
If a filepath is provided for filepath_or_buffer, map the file object
directly onto memory and access the data directly from there. Using this
option can improve performance because there is no longer any I/O overhead.
float_precision : str, optional
Specifies which converter the C engine should use for floating-point
values. The options are None for the ordinary converter,
high for the high-precision converter, and round_trip for the
round-trip converter.

Returns
-------
DataFrame or TextParser
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.

--------
to_csv : Write DataFrame to a comma-separated values (csv) file.

Examples
--------
File:      c:\users\17862\anaconda3\lib\site-packages\pandas\io\parsers.py
Type:      function


Even though this functions has many parameters, we will simply pass it the location of the text file.

Location = C:\Users\ENTER_USER_NAME.xy\startups\births1880.csv

Note: Depending on where you save your notebooks, you may need to modify the location above.

In [10]:
Location = r'C:\notebooks\pandas\births1880.csv'


Notice the r before the string. Since the slashes are special characters, prefixing the string with a r will escape the whole string.

In [11]:
df

Out[11]:
Bob 968
0 Jessica 155
1 Mary 77
2 John 578
3 Mel 973

This brings us to the first problem of the exercise. The read_csv function treated the first record in the csv file as the header names. This is obviously not correct since the text file did not provide us with header names.

To correct this we will pass the header parameter to the read_csv function and set it to None (means null in python).

In [12]:
df = pd.read_csv(Location, header=None)
df

Out[12]:
0 1
0 Bob 968
1 Jessica 155
2 Mary 77
3 John 578
4 Mel 973

If we wanted to give the columns specific names, we would have to pass another parameter called names. We can also omit the header parameter.

In [13]:
df = pd.read_csv(Location, names=['Names','Births'])
df

Out[13]:
Names Births
0 Bob 968
1 Jessica 155
2 Mary 77
3 John 578
4 Mel 973

You can think of the numbers [0,1,2,3,4] as the row numbers in an Excel file. In pandas these are part of the index of the dataframe. You can think of the index as the primary key of a sql table with the exception that an index is allowed to have duplicates.

[Names, Births] can be though of as column headers similar to the ones found in an Excel spreadsheet or sql database.

Delete the csv file now that we are done using it.

In [14]:
import os
os.remove(Location)


## Prepare Data¶

The data we have consists of baby names and the number of births in the year 1880. We already know that we have 5 records and none of the records are missing (non-null values).

The Names column at this point is of no concern since it most likely is just composed of alpha numeric strings (baby names). There is a chance of bad data in this column but we will not worry about that at this point of the analysis. The Births column should just contain integers representing the number of babies born in a specific year with a specific name. We can check if the all the data is of the data type integer. It would not make sense to have this column have a data type of float. I would not worry about any possible outliers at this point of the analysis.

Realize that aside from the check we did on the "Names" column, briefly looking at the data inside the dataframe should be as far as we need to go at this stage of the game. As we continue in the data analysis life cycle we will have plenty of opportunities to find any issues with the data set.

In [15]:
# Check data type of the columns
df.dtypes

Out[15]:
Names     object
Births     int64
dtype: object
In [16]:
# Check data type of Births column
df.Births.dtype

Out[16]:
dtype('int64')

As you can see the Births column is of type int64, thus no floats (decimal numbers) or alpha numeric characters will be present in this column.

## Analyze Data¶

To find the most popular name or the baby name with the higest birth rate, we can do one of the following.

• Sort the dataframe and select the top row
• Use the max() attribute to find the maximum value
In [17]:
# Method 1:
Sorted = df.sort_values(['Births'], ascending=False)

Out[17]:
Names Births
4 Mel 973
In [18]:
# Method 2:
df['Births'].max()

Out[18]:
973

## Present Data¶

Here we can plot the Births column and label the graph to show the end user the highest point on the graph. In conjunction with the table, the end user has a clear picture that Mel is the most popular baby name in the data set.

plot() is a convinient attribute where pandas lets you painlessly plot the data in your dataframe. We learned how to find the maximum value of the Births column in the previous section. Now to find the actual baby name of the 973 value looks a bit tricky, so lets go over it.

Explain the pieces:
df['Names'] - This is the entire list of baby names, the entire Names column
df['Births'] - This is the entire list of Births in the year 1880, the entire Births column
df['Births'].max() - This is the maximum value found in the Births column

[df['Births'] == df['Births'].max()] IS EQUAL TO [Find all of the records in the Births column where it is equal to 973]
df['Names'][df['Births'] == df['Births'].max()] IS EQUAL TO Select all of the records in the Names column WHERE [The Births column is equal to 973]

An alternative way could have been to use the Sorted dataframe:

The str() function simply converts an object into a string.

In [19]:
# Create graph
df['Births'].plot()

# Maximum value in the data set
MaxValue = df['Births'].max()

# Name associated with the maximum value
MaxName = df['Names'][df['Births'] == df['Births'].max()].values

# Text to display on graph
Text = str(MaxValue) + " - " + MaxName

plt.annotate(Text, xy=(1, MaxValue),
xytext=(25, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points',
arrowprops=dict(arrowstyle='-|>'))

print("The most popular name")
df[df['Births'] == df['Births'].max()]

The most popular name