This is how to set the colors in pandas

mapper = {v: k for k, v in enumerate(colors.unique())}

ccolors.cat.reorder_categories(mapper).cat.codes

Here is how to change the color of negative values to red in pandas.

def color_negative_red(val):
color = 'red' if val < 0 else 'black'
return 'color: %s' % color

df = df.style.applymap(color_negative_red)

This is the python way of calculating the date that is 14 days ago.

import datetime
(datetime.datetime.now() - datetime.timedelta(days = 14)).date()

And this is pandas way of getting the same results:

import pandas as pd
(pd.datetime.today() - pd.offsets.Day(14)).floor('D')

from functools import partialmethod
pd.DataFrame.head = partialmethod(pd.DataFrame.head, n=3)

This will return the mixed datatypes but this can be slow.

df.applymap(type).nunique() > 1

Better approach pythonic approach would be...

from pandas.api.types import infer_dtype

infer_dtype(df.A)
'mixed-integer'

df.apply(lambda x: 'mixed' in infer_dtype(x))

Here are important python objects and their methods
int __mul__, __add__, __eq__, __ge__, __mod__, __pow__
float .is_integer()
string .upper(), .endswith(), .split()
bool .numerator .__abs__()
list .append, .pop, __len__() / slicing
tuple
dict .items(), .keys(), .values() / slicing
pd.Series .sum(), .stack(), .groupby(), .resample(), .sort_values(), .sort_index(), .iloc
pd.DataFrame

Here is how to read a JSON file into pandas dataframe.

import json
import pandas as pd

with open('/tmp/aclImdb/movie_data/gv.json', 'r') as f:
data = json.load(f)

df=pd.DataFrame(data['data'])
df.to_excel('gov.xlsx')