Data tabulation from json files¶

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Url: https://api.covid19india.org/states_daily.json ¶

!wget https://api.covid19india.org/states_daily.json - Unix way of getting data

url = 'https://api.covid19india.org/states_daily.json'

import urllib.request

urllib.request.urlretrieve(url, 'data.json')

('data.json', <http.client.HTTPMessage at 0x7f5032a97710>)

df = pd.read_json('data.json')

df

the above format is not what is required, but in tabular format, hence¶

load it into dictionary, and then
load it into pandas DataFrame

import json

with open('data.json') as f:
    data = json.load(f)
data

# output is displayed in a very long list of values in json file format

data = data['states_daily']

noralise function is used to format data into resepective rows and cols¶

df = pd.json_normalize(data)

df

now, the job is to format this into tabular form¶

with index as date, and status
column removed

df.date = pd.to_datetime(df.date)

df

limit dataframe only to Confirmed records¶

df = df[df.status == "Confirmed"]

df

df.drop(['status'], axis = 1, inplace = True)

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,

df.set_index('date', inplace = True)

df

df.drop('dateymd', inplace = True, axis=1)

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,

df

because the data is from json, all the datatypes are in 'Object' instead of int64,¶

convert object datatype to int64

df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 521 entries, 2020-03-14 to 2021-08-16
Data columns (total 39 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   an      521 non-null    object
 1   ap      521 non-null    object
 2   ar      521 non-null    object
 3   as      521 non-null    object
 4   br      521 non-null    object
 5   ch      521 non-null    object
 6   ct      521 non-null    object
 7   dd      521 non-null    object
 8   dl      521 non-null    object
 9   dn      521 non-null    object
 10  ga      521 non-null    object
 11  gj      521 non-null    object
 12  hp      521 non-null    object
 13  hr      521 non-null    object
 14  jh      521 non-null    object
 15  jk      521 non-null    object
 16  ka      521 non-null    object
 17  kl      521 non-null    object
 18  la      521 non-null    object
 19  ld      521 non-null    object
 20  mh      521 non-null    object
 21  ml      521 non-null    object
 22  mn      521 non-null    object
 23  mp      521 non-null    object
 24  mz      521 non-null    object
 25  nl      521 non-null    object
 26  or      521 non-null    object
 27  pb      521 non-null    object
 28  py      521 non-null    object
 29  rj      521 non-null    object
 30  sk      521 non-null    object
 31  tg      521 non-null    object
 32  tn      521 non-null    object
 33  tr      521 non-null    object
 34  tt      521 non-null    object
 35  un      521 non-null    object
 36  up      521 non-null    object
 37  ut      521 non-null    object
 38  wb      521 non-null    object
dtypes: object(39)
memory usage: 162.8+ KB

df.tn

date
2020-03-14       1
2020-03-15       0
2020-03-16       0
2020-03-17       0
2020-03-18       1
              ... 
2021-08-12    1942
2021-08-13    1933
2021-08-14    1916
2021-08-15    1896
2021-08-16    1851
Name: tn, Length: 521, dtype: object

pd.to_numeric(df.tn)

date
2020-03-14       1
2020-03-15       0
2020-03-16       0
2020-03-17       0
2020-03-18       1
              ... 
2021-08-12    1942
2021-08-13    1933
2021-08-14    1916
2021-08-15    1896
2021-08-16    1851
Name: tn, Length: 521, dtype: int64

df = df.apply(pd.to_numeric)

df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 521 entries, 2020-03-14 to 2021-08-16
Data columns (total 39 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   an      521 non-null    int64
 1   ap      521 non-null    int64
 2   ar      521 non-null    int64
 3   as      521 non-null    int64
 4   br      521 non-null    int64
 5   ch      521 non-null    int64
 6   ct      521 non-null    int64
 7   dd      521 non-null    int64
 8   dl      521 non-null    int64
 9   dn      521 non-null    int64
 10  ga      521 non-null    int64
 11  gj      521 non-null    int64
 12  hp      521 non-null    int64
 13  hr      521 non-null    int64
 14  jh      521 non-null    int64
 15  jk      521 non-null    int64
 16  ka      521 non-null    int64
 17  kl      521 non-null    int64
 18  la      521 non-null    int64
 19  ld      521 non-null    int64
 20  mh      521 non-null    int64
 21  ml      521 non-null    int64
 22  mn      521 non-null    int64
 23  mp      521 non-null    int64
 24  mz      521 non-null    int64
 25  nl      521 non-null    int64
 26  or      521 non-null    int64
 27  pb      521 non-null    int64
 28  py      521 non-null    int64
 29  rj      521 non-null    int64
 30  sk      521 non-null    int64
 31  tg      521 non-null    int64
 32  tn      521 non-null    int64
 33  tr      521 non-null    int64
 34  tt      521 non-null    int64
 35  un      521 non-null    int64
 36  up      521 non-null    int64
 37  ut      521 non-null    int64
 38  wb      521 non-null    int64
dtypes: int64(39)
memory usage: 162.8 KB

df.tail(7)

Styling tabulation¶

df7 = df.tail(7)

df7.style

color negative values in red¶

useful link for CSS style colors: http://web.simmons.edu/~grabiner/comm244/weekthree/css-colors.html ¶

def color_red_negative(x):
    color = 'red' if x < 0 else 'white'
    return 'color: ' + color

df7.style.applymap(color_red_negative)

df7.drop('un', inplace = True, axis=1)

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,

df7.style.highlight_max('red')

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py in __call__(self, obj)
    332                 pass
    333             else:
--> 334                 return printer(obj)
    335             # Finally look for special method names
    336             method = get_real_method(obj, self.print_method)

/usr/local/lib/python3.7/dist-packages/google/colab/_reprs.py in new_formatter(dataframe)
     86 
     87   def new_formatter(dataframe):
---> 88     return dataframe.set_table_attributes('class="dataframe"')._repr_html_()  # pylint: disable=protected-access
     89 
     90   _original_df_formatters[key] = formatters[key].for_type_by_name(

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style.py in _repr_html_(self)
    214         Hooks into Jupyter notebook rich display system.
    215         """
--> 216         return self.render()
    217 
    218     def render(

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style.py in render(self, sparse_index, sparse_columns, **kwargs)
    270         if sparse_columns is None:
    271             sparse_columns = get_option("styler.sparse.columns")
--> 272         return self._render_html(sparse_index, sparse_columns, **kwargs)
    273 
    274     def set_tooltips(

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style_render.py in _render_html(self, sparse_index, sparse_columns, **kwargs)
    119         Generates a dict with necessary kwargs passed to jinja2 template.
    120         """
--> 121         self._compute()
    122         # TODO: namespace all the pandas keys
    123         d = self._translate(sparse_index, sparse_columns)

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style_render.py in _compute(self)
    158         r = self
    159         for func, args, kwargs in self._todo:
--> 160             r = func(self)(*args, **kwargs)
    161         return r
    162 

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style.py in _apply(self, func, axis, subset, **kwargs)
   1051         subset = slice(None) if subset is None else subset
   1052         subset = non_reducing_slice(subset)
-> 1053         data = self.data.loc[subset]
   1054         if axis is not None:
   1055             result = data.apply(func, axis=axis, result_type="expand", **kwargs)

/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in __getitem__(self, key)
    923                 with suppress(KeyError, IndexError):
    924                     return self.obj._get_value(*key, takeable=self._takeable)
--> 925             return self._getitem_tuple(key)
    926         else:
    927             # we by definition only have the 0th axis

/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
   1107             return self._multi_take(tup)
   1108 
-> 1109         return self._getitem_tuple_same_dim(tup)
   1110 
   1111     def _get_label(self, label, axis: int):

/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in _getitem_tuple_same_dim(self, tup)
    804                 continue
    805 
--> 806             retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
    807             # We should never have retval.ndim < self.ndim, as that should
    808             #  be handled by the _getitem_lowerdim call above.

/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
   1151                     raise ValueError("Cannot index with multidimensional key")
   1152 
-> 1153                 return self._getitem_iterable(key, axis=axis)
   1154 
   1155             # nested tuple slicing

/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
   1091 
   1092         # A collection of keys
-> 1093         keyarr, indexer = self._get_listlike_indexer(key, axis)
   1094         return self.obj._reindex_with_indexers(
   1095             {axis: [keyarr, indexer]}, copy=True, allow_dups=True

/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis)
   1312             keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
   1313 
-> 1314         self._validate_read_indexer(keyarr, indexer, axis)
   1315 
   1316         if needs_i8_conversion(ax.dtype) or isinstance(

/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis)
   1372                 if use_interval_msg:
   1373                     key = list(key)
-> 1374                 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
   1375 
   1376             not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())

KeyError: "None of [Index(['red'], dtype='object')] are in the [columns]"

<pandas.io.formats.style.Styler at 0x7f50318bcd90>

df7.style.highlight_max(color='red')

df7.style.highlight_max(color='red')

df7.drop(['dd', 'ld'], axis=1, inplace=True)

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,

df7.style.highlight_max(color='red').highlight_min(color='green')

df7.drop('tt', axis = 1,inplace = True)

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,

def color_cyan_negative(x):
    color = 'cyan' if x == 0 else 'while'
    return 'color:' + color

df7.style.applymap(color_cyan_negative)

df7.style.highlight_max(color='green')

df7.drop('dd', inplace = True, axis=1)

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,

df7

df7.style.highlight_max(color='red')

df7.style.highlight_min(color='green')

highlight both high and low values in red and green respectively¶

df7.style.highlight_max(color='red').highlight_min(color='green')

Write a function to find the max value and make it bold¶

apply() is used to apply a function along an axis or column of the DataFrame or on values of Series.
applymap() is used to apply a function to each element in a DataFrame.
map() is used to substitute each value in a Series with another value

df7

def print_max(x):
    is_max = (x == x.max())
    print(is_max)

df7.apply(print_max)

def make_max_bold(x):
    is_max = (x == x.max())
    return ['font-weight: bold' if y else "" for y in is_max]

df7.style.apply(make_max_bold)

df7.style.apply(make_max_bold).highlight_min(color='green', axis=0)

df7.style.apply(make_max_bold).highlight_max(color='green', axis=1)

df7.style.apply(make_max_bold).highlight_max(color='red', axis=1)

df7.style.background_gradient(cmap = 'Blues')

displays the list of all the valid values for cmap¶

light - denoting small number and
dark color - denoting large number

df7.style.background_gradient(cmap = 'blue')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py in __call__(self, obj)
    332                 pass
    333             else:
--> 334                 return printer(obj)
    335             # Finally look for special method names
    336             method = get_real_method(obj, self.print_method)

/usr/local/lib/python3.7/dist-packages/google/colab/_reprs.py in new_formatter(dataframe)
     86 
     87   def new_formatter(dataframe):
---> 88     return dataframe.set_table_attributes('class="dataframe"')._repr_html_()  # pylint: disable=protected-access
     89 
     90   _original_df_formatters[key] = formatters[key].for_type_by_name(

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style.py in _repr_html_(self)
    214         Hooks into Jupyter notebook rich display system.
    215         """
--> 216         return self.render()
    217 
    218     def render(

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style.py in render(self, sparse_index, sparse_columns, **kwargs)
    270         if sparse_columns is None:
    271             sparse_columns = get_option("styler.sparse.columns")
--> 272         return self._render_html(sparse_index, sparse_columns, **kwargs)
    273 
    274     def set_tooltips(

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style_render.py in _render_html(self, sparse_index, sparse_columns, **kwargs)
    119         Generates a dict with necessary kwargs passed to jinja2 template.
    120         """
--> 121         self._compute()
    122         # TODO: namespace all the pandas keys
    123         d = self._translate(sparse_index, sparse_columns)

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style_render.py in _compute(self)
    158         r = self
    159         for func, args, kwargs in self._todo:
--> 160             r = func(self)(*args, **kwargs)
    161         return r
    162 

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style.py in _apply(self, func, axis, subset, **kwargs)
   1053         data = self.data.loc[subset]
   1054         if axis is not None:
-> 1055             result = data.apply(func, axis=axis, result_type="expand", **kwargs)
   1056             result.columns = data.columns
   1057         else:

/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py in apply(self, func, axis, raw, result_type, args, **kwargs)
   8738             kwargs=kwargs,
   8739         )
-> 8740         return op.apply()
   8741 
   8742     def applymap(

/usr/local/lib/python3.7/dist-packages/pandas/core/apply.py in apply(self)
    686             return self.apply_raw()
    687 
--> 688         return self.apply_standard()
    689 
    690     def agg(self):

/usr/local/lib/python3.7/dist-packages/pandas/core/apply.py in apply_standard(self)
    810 
    811     def apply_standard(self):
--> 812         results, res_index = self.apply_series_generator()
    813 
    814         # wrap results

/usr/local/lib/python3.7/dist-packages/pandas/core/apply.py in apply_series_generator(self)
    826             for i, v in enumerate(series_gen):
    827                 # ignore SettingWithCopy here in case the user mutates
--> 828                 results[i] = self.f(v)
    829                 if isinstance(results[i], ABCSeries):
    830                     # If we have a view on v, we need to make a copy because

/usr/local/lib/python3.7/dist-packages/pandas/core/apply.py in f(x)
    129 
    130             def f(x):
--> 131                 return func(x, *args, **kwargs)
    132 
    133         else:

/usr/local/lib/python3.7/dist-packages/pandas/io/formats/style.py in _background_gradient(data, cmap, low, high, text_color_threshold, vmin, vmax, gmap, text_only)
   2816         # extend lower / upper bounds, compresses color range
   2817         norm = colors.Normalize(smin - (rng * low), smax + (rng * high))
-> 2818         rgbas = plt.cm.get_cmap(cmap)(norm(gmap))
   2819 
   2820         def relative_luminance(rgba) -> float:

/usr/local/lib/python3.7/dist-packages/matplotlib/cm.py in get_cmap(name, lut)
    132     if isinstance(name, colors.Colormap):
    133         return name
--> 134     cbook._check_in_list(sorted(cmap_d), name=name)
    135     if lut is None:
    136         return cmap_d[name]

/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py in _check_in_list(_values, **kwargs)
   2143             raise ValueError(
   2144                 "{!r} is not a valid value for {}; supported values are {}"
-> 2145                 .format(v, k, ', '.join(map(repr, values))))
   2146 
   2147 

ValueError: 'blue' is not a valid value for name; supported values are 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'crest', 'crest_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'flare', 'flare_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magm...

<pandas.io.formats.style.Styler at 0x7f502c72a410>

df7.style.background_gradient(cmap = 'crest')

df7.style.background_gradient(cmap = "Reds", axis = 1)

to highlight only a subset of the values then¶

df7.style.background_gradient(cmap = "Blues", subset = ['ka', 'dl', 'mh', 'tn'])

df7.style.bar()

df7.style.bar(subset=['dl', 'mh', 'tn'])

select only a subset of dataset and apply style only to that subset¶

df7[['mh', 'tn', 'dl']].style.bar()

df7[['mh', 'dl', 'tn']].style.bar(subset=['dl'], color='red').bar(['mh'], color='blue').bar(['tn'], color='green')

!pip install nbconvert

%shell jupyter nbconvert --to html /content/Padhai_data_tabulation.ipynb

[NbConvertApp] Converting notebook /content/Padhai_data_tabulation.ipynb to html
[NbConvertApp] Writing 3785731 bytes to /content/Padhai_data_tabulation.html

	states_daily
0	{'an': '0', 'ap': '1', 'ar': '0', 'as': '0', '...
1	{'an': '0', 'ap': '0', 'ar': '0', 'as': '0', '...
2	{'an': '0', 'ap': '0', 'ar': '0', 'as': '0', '...
3	{'an': '0', 'ap': '0', 'ar': '0', 'as': '0', '...
4	{'an': '0', 'ap': '0', 'ar': '0', 'as': '0', '...
...	...
1558	{'an': '2', 'ap': '1835', 'ar': '255', 'as': '...
1559	{'an': '0', 'ap': '16', 'ar': '0', 'as': '10',...
1560	{'an': '1', 'ap': '909', 'ar': '165', 'as': '7...
1561	{'an': '0', 'ap': '1543', 'ar': '249', 'as': '...
1562	{'an': '0', 'ap': '13', 'ar': '0', 'as': '10',...

Consulting - Quality

PANDAS VISUALISATION

Padhai - Read Json, Visualise

Data tabulation from json files¶

Url: https://api.covid19india.org/states_daily.json ¶

the above format is not what is required, but in tabular format, hence¶

noralise function is used to format data into resepective rows and cols¶

now, the job is to format this into tabular form¶

limit dataframe only to Confirmed records¶

because the data is from json, all the datatypes are in 'Object' instead of int64,¶

Styling tabulation¶

color negative values in red¶

useful link for CSS style colors: http://web.simmons.edu/~grabiner/comm244/weekthree/css-colors.html ¶

highlight both high and low values in red and green respectively¶

Write a function to find the max value and make it bold¶

displays the list of all the valid values for cmap¶

to highlight only a subset of the values then¶

select only a subset of dataset and apply style only to that subset¶

	an	ap	ar	as	br	ch	ct	date	dateymd	dd	...	sk	status	tg	tn	tr	tt	un	up	ut	wb
0	0	1	0	0	0	0	0	14-Mar-20	2020-03-14	0	...	0	Confirmed	1	1	0	81	0	12	0	0
1	0	0	0	0	0	0	0	14-Mar-20	2020-03-14	0	...	0	Recovered	0	0	0	9	0	4	0	0
2	0	0	0	0	0	0	0	14-Mar-20	2020-03-14	0	...	0	Deceased	0	0	0	2	0	0	0	0
3	0	0	0	0	0	0	0	15-Mar-20	2020-03-15	0	...	0	Confirmed	2	0	0	27	0	1	0	0
4	0	0	0	0	0	0	0	15-Mar-20	2020-03-15	0	...	0	Recovered	1	0	0	4	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1558	2	1835	255	857	38	1	114	15-Aug-21	2021-08-15	0	...	213	Recovered	582	1842	253	35936	0	36	31	709
1559	0	16	0	10	0	0	1	15-Aug-21	2021-08-15	0	...	0	Deceased	1	23	4	421	0	1	0	12
1560	1	909	165	758	14	2	68	16-Aug-21	2021-08-16	0	...	20	Confirmed	405	1851	52	24696	0	17	18	502
1561	0	1543	249	1014	42	3	224	16-Aug-21	2021-08-16	0	...	147	Recovered	577	1911	223	36871	0	36	54	691
1562	0	13	0	10	0	0	1	16-Aug-21	2021-08-16	0	...	0	Deceased	3	28	1	438	0	1	1	9

	an	ap	ar	as	br	ch	ct	dateymd	dd	dl	...	rj	sk	tg	tn	tr	tt	un	up	ut	wb
date
2020-03-14	0	1	0	0	0	0	0	2020-03-14	0	7	...	3	0	1	1	0	81	0	12	0	0
2020-03-15	0	0	0	0	0	0	0	2020-03-15	0	0	...	1	0	2	0	0	27	0	1	0	0
2020-03-16	0	0	0	0	0	0	0	2020-03-16	0	0	...	0	0	1	0	0	15	0	0	1	0
2020-03-17	0	0	0	0	0	0	0	2020-03-17	0	1	...	0	0	1	0	0	11	0	2	0	1
2020-03-18	0	0	0	0	0	0	0	2020-03-18	0	2	...	3	0	8	1	0	37	0	2	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-08-12	0	1859	180	935	43	12	98	2021-08-12	0	49	...	17	100	453	1942	140	40081	0	15	24	747
2021-08-13	0	1746	166	763	47	15	77	2021-08-13	0	50	...	24	150	427	1933	180	38761	0	25	27	739
2021-08-14	0	1535	161	755	39	4	83	2021-08-14	0	50	...	14	129	420	1916	137	36135	0	42	19	705
2021-08-15	0	1506	48	411	28	1	49	2021-08-15	0	53	...	18	152	245	1896	142	33245	0	30	16	673
2021-08-16	1	909	165	758	14	2	68	2021-08-16	0	27	...	11	20	405	1851	52	24696	0	17	18	502

	an	ap	ar	as	br	ch	ct	dd	dl	dn	...	rj	sk	tg	tn	tr	tt	un	up	ut	wb
date
2021-08-10	2	1461	233	929	44	8	112	0	52	1	...	11	110	494	1893	205	38376	0	19	39	639
2021-08-11	0	1869	188	886	47	5	83	0	37	0	...	19	157	482	1964	244	41586	0	24	40	700
2021-08-12	0	1859	180	935	43	12	98	0	49	1	...	17	100	453	1942	140	40081	0	15	24	747
2021-08-13	0	1746	166	763	47	15	77	0	50	0	...	24	150	427	1933	180	38761	0	25	27	739
2021-08-14	0	1535	161	755	39	4	83	0	50	0	...	14	129	420	1916	137	36135	0	42	19	705
2021-08-15	0	1506	48	411	28	1	49	0	53	0	...	18	152	245	1896	142	33245	0	30	16	673
2021-08-16	1	909	165	758	14	2	68	0	27	2	...	11	20	405	1851	52	24696	0	17	18	502

	an	ap	ar	as	br	ch	ct	dd	dl	dn	ga	gj	hp	hr	jh	jk	ka	kl	la	ld	mh	ml	mn	mp	mz	nl	or	pb	py	rj	sk	tg	tn	tr	tt	un	up	ut	wb
date
2021-08-10 00:00:00	2	1461	233	929	44	8	112	0	52	1	141	21	419	23	34	113	1338	21119	7	6	5609	411	633	10	937	102	1041	59	101	11	110	494	1893	205	38376	0	19	39	639
2021-08-11 00:00:00	0	1869	188	886	47	5	83	0	37	0	103	16	374	16	14	114	1826	23500	10	5	5560	463	606	10	863	64	1078	105	114	19	157	482	1964	244	41586	0	24	40	700
2021-08-12 00:00:00	0	1859	180	935	43	12	98	0	49	1	88	17	354	16	44	176	1857	21445	9	6	6388	429	677	8	576	80	1107	80	109	17	100	453	1942	140	40081	0	15	24	747
2021-08-13 00:00:00	0	1746	166	763	47	15	77	0	50	0	67	23	333	26	32	129	1669	20452	6	4	6686	384	522	9	575	81	1193	88	113	24	150	427	1933	180	38761	0	25	27	739
2021-08-14 00:00:00	0	1535	161	755	39	4	83	0	50	0	88	25	284	14	28	135	1632	19451	9	7	5787	343	439	16	524	61	1132	50	101	14	129	420	1916	137	36135	0	42	19	705
2021-08-15 00:00:00	0	1506	48	411	28	1	49	0	53	0	75	16	182	22	27	97	1431	18582	6	3	4797	420	602	13	467	54	1058	46	79	18	152	245	1896	142	33245	0	30	16	673
2021-08-16 00:00:00	1	909	165	758	14	2	68	0	27	2	62	14	276	22	35	77	1065	12294	4	6	4145	352	275	7	249	44	868	30	49	11	20	405	1851	52	24696	0	17	18	502

Consulting - Quality

PANDAS VISUALISATION

Padhai - Read Json, Visualise

Data tabulation from json files¶

Url: https://api.covid19india.org/states_daily.json¶

the above format is not what is required, but in tabular format, hence¶

noralise function is used to format data into resepective rows and cols¶

now, the job is to format this into tabular form¶

limit dataframe only to Confirmed records¶

because the data is from json, all the datatypes are in 'Object' instead of int64,¶

Styling tabulation¶

color negative values in red¶

useful link for CSS style colors: http://web.simmons.edu/~grabiner/comm244/weekthree/css-colors.html¶

highlight both high and low values in red and green respectively¶

Write a function to find the max value and make it bold¶

displays the list of all the valid values for cmap¶

to highlight only a subset of the values then¶

select only a subset of dataset and apply style only to that subset¶

Url: https://api.covid19india.org/states_daily.json ¶

useful link for CSS style colors: http://web.simmons.edu/~grabiner/comm244/weekthree/css-colors.html ¶