DataFrame object

The pandas DataFrame object a pandas series represents a single array of values, with an index label for each value.if you want to have more than one series of data that is aligned by a common index, then a Pandas DataFrame is used.

import pandas as pd
from pandas import DataFrame, Series

dates = pd.date_range('2019-05-18', '2019-05-25')
temp_chennai = Series([36, 37, 36, 37, 37, 37, 37, 37],
                     index = dates)
temp_delhi = Series([34, 39, 41, 41, 41, 41, 41, 42],
                   index = dates)

# create a DataFrame from the two Series objects temp_chennai and temp_delhi
# and give them column names
temps_df = DataFrame({
    "chennai" : temp_chennai,
     "Delhi" : temp_delhi
}) 
temps_df

temps_df['chennai'] # get the column with the name chennai

2019-05-18    36
2019-05-19    37
2019-05-20    36
2019-05-21    37
2019-05-22    37
2019-05-23    37
2019-05-24    37
2019-05-25    37
Freq: D, Name: chennai, dtype: int64

temps_df['Delhi'] # get the column with the name Delhi

2019-05-18    34
2019-05-19    39
2019-05-20    41
2019-05-21    41
2019-05-22    41
2019-05-23    41
2019-05-24    41
2019-05-25    42
Freq: D, Name: Delhi, dtype: int64

temps_df.chennai # gretrieve the chennai column through property syntax

2019-05-18    36
2019-05-19    37
2019-05-20    36
2019-05-21    37
2019-05-22    37
2019-05-23    37
2019-05-24    37
2019-05-25    37
Freq: D, Name: chennai, dtype: int64

temp_diffs = abs(temps_df.chennai - temps_df.Delhi)
temps_df['Difference'] = temp_diffs
temps_df

temps_df.columns # get columns 

Index(['chennai', 'Delhi', 'Difference'], dtype='object')

temps_df.Difference[1:4]

2019-05-19    2
2019-05-20    5
2019-05-21    4
Freq: D, Name: Difference, dtype: int64

temps_df.iloc[0] # get the row array postition 0

chennai       36
Delhi         34
Difference     2
Name: 2019-05-18 00:00:00, dtype: int64

temps_df.ix[1].index

/home/mmblack/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
  """Entry point for launching an IPython kernel.





Index(['chennai', 'Delhi', 'Difference'], dtype='object')

temps_df.loc['2019-05-19']

chennai       37
Delhi         39
Difference     2
Name: 2019-05-19 00:00:00, dtype: int64

temps_df.iloc[[1, 3, 5]] # select only rows 1, 3, 5

temps_df.Delhi >40 # which values in Delhi column are great then 40

2019-05-18    False
2019-05-19    False
2019-05-20     True
2019-05-21     True
2019-05-22     True
2019-05-23     True
2019-05-24     True
2019-05-25     True
Freq: D, Name: Delhi, dtype: bool

temps_df[temps_df.Delhi > 40] # return the rows where the temps for delhi great then 40