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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.
# Import all libraries needed for the tutorial
# General syntax to import specific functions in a library:
##from (library) import (specific library function)
from pandas import DataFrame, read_csv
# 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
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
The data set will consist of 5 baby names and the number of births recorded for that year (1880).
# 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.
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:
BabyDataSet = list(zip(names,births))
BabyDataSet
[('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*.
df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])
df
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.
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, header: Union[bool, List[str]] = True, 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 additional compression options. .. 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. See Also -------- read_csv : Load a CSV file into a DataFrame. to_excel : Write DataFrame to an Excel file. Examples -------- >>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'], ... 'mask': ['red', 'purple'], ... 'weapon': ['sai', 'bo staff']}) >>> df.to_csv(index=False) 'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n' 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.
df.to_csv('births1880.csv',index=False,header=False)
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.
read_csv?
Signature: read_csv( filepath_or_buffer: Union[str, pathlib.Path, IO[~AnyStr]], sep=',', delimiter=None, header='infer', 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, error_bad_lines=True, warn_bad_lines=True, 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 of reading a large file. 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. .. versionadded:: 0.25.0 iterator : bool, default False Return TextFileReader object for iteration or getting chunks with ``get_chunk()``. chunksize : int, optional Return TextFileReader object for iteration. 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 treated as the header. 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. error_bad_lines : bool, default True 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. warn_bad_lines : bool, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. 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. See Also -------- to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- >>> pd.read_csv('data.csv') # doctest: +SKIP 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.
Location = r'C:\notebooks\pandas\births1880.csv'
df = pd.read_csv(Location)
Notice the *r* before the string. Since the slashes are special characters, prefixing the string with a *r* will escape the whole string.
df
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).
df = pd.read_csv(Location, header=None)
df
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.
df = pd.read_csv(Location, names=['Names','Births'])
df
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.
import os
os.remove(Location)
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.
# Check data type of the columns
df.dtypes
Names object Births int64 dtype: object
# Check data type of Births column
df.Births.dtype
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.
To find the most popular name or the baby name with the higest birth rate, we can do one of the following.
# Method 1:
Sorted = df.sort_values(['Births'], ascending=False)
Sorted.head(1)
Names | Births | |
---|---|---|
4 | Mel | 973 |
# Method 2:
df['Births'].max()
973
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:
Sorted['Names'].head(1).value
The *str()* function simply converts an object into a string.
# 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
# Add text to graph
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()]
#Sorted.head(1) can also be used
The most popular name
Names | Births | |
---|---|---|
4 | Mel | 973 |
This tutorial was created by HEDARO