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Project Description
# EasyFrames


## Loading datasets

This package makes it easier to perform some basic operations using a Pandas dataframe. For example, suppose you have the following datasets:

```
age educ fridge has_car hh house_rooms id male prov weighthh
0 44 pri yes 1 1 3 1 1 BC 2
1 43 bach yes 1 1 3 2 0 BC 2
2 13 pri yes 1 1 3 3 1 BC 2
3 70 hi no 1 2 2 1 1 Alberta 3
4 23 bach yes 0 3 1 1 1 BC 2
5 20 sec yes 0 3 1 2 0 BC 2
6 37 hi no 1 4 3 1 1 Alberta 3
7 35 hi no 1 4 3 2 0 Alberta 3
8 8 pri no 1 4 3 3 0 Alberta 3
9 15 pri no 1 4 3 4 0 Alberta 3
```
```
has_fence hh
0 1 2
1 0 4
2 1 5
3 1 6
4 0 7
```
```
empl hh id
0 ue 1 1
1 ft 1 2
2 pt 1 4
3 pt 2 1
4 ft 5 1
5 pt 5 2
6 se 4 1
7 ft 4 2
8 se 4 5
```

If you have these datasets already in Stata .dta files, then using easyframes you can load them in like this:
```
myhhkit = hhkit('mydataset.dta', encoding="latin-1")
```
To make this demonstration easy to follow, I will instead load the data from the following Pandas Series from Dicts:
```
df_master = pd.DataFrame(
{'educ': {0: 'pri', 1: 'bach', 2: 'pri', 3: 'hi', 4: 'bach', 5: 'sec',
6: 'hi', 7: 'hi', 8: 'pri', 9: 'pri'},
'hh': {0: 1, 1: 1, 2: 1, 3: 2, 4: 3, 5: 3, 6: 4, 7: 4, 8: 4, 9: 4},
'id': {0: 1, 1: 2, 2: 3, 3: 1, 4: 1, 5: 2, 6: 1, 7: 2, 8: 3, 9: 4},
'has_car': {0: 1, 1: 1, 2: 1, 3: 1, 4: 0, 5: 0, 6: 1, 7: 1, 8: 1, 9: 1},
'weighthh': {0: 2, 1: 2, 2: 2, 3: 3, 4: 2, 5: 2, 6: 3, 7: 3, 8: 3, 9: 3},
'house_rooms': {0: 3, 1: 3, 2: 3, 3: 2, 4: 1, 5: 1, 6: 3, 7: 3, 8: 3, 9: 3},
'prov': {0: 'BC', 1: 'BC', 2: 'BC', 3: 'Alberta', 4: 'BC', 5: 'BC', 6: 'Alberta',
7: 'Alberta', 8: 'Alberta', 9: 'Alberta'},
'age': {0: 44, 1: 43, 2: 13, 3: 70, 4: 23, 5: 20, 6: 37, 7: 35, 8: 8, 9: 15},
'fridge': {0: 'yes', 1: 'yes', 2: 'yes', 3: 'no', 4: 'yes', 5: 'yes', 6: 'no',
7: 'no', 8: 'no', 9: 'no'},
'male': {0: 1, 1: 0, 2: 1, 3: 1, 4: 1, 5: 0, 6: 1, 7: 0, 8: 0, 9: 0}})
df_using_hh = pd.DataFrame(
{'hh': {0: 2, 1: 4, 2: 5, 3: 6, 4: 7},
'has_fence': {0: 1, 1: 0, 2: 1, 3: 1, 4: 0}
})
df_using_ind = pd.DataFrame(
{'empl': {0: 'ue', 1: 'ft', 2: 'pt', 3: 'pt', 4: 'ft', 5: 'pt',
6: 'se', 7: 'ft', 8: 'se'},
'hh': {0: 1, 1: 1, 2: 1, 3: 2, 4: 5, 5: 5, 6: 4, 7: 4, 8: 4},
'id': {0: 1, 1: 2, 2: 4, 3: 1, 4: 1, 5: 2, 6: 1, 7: 2, 8: 5}
})
```
Here is how you can load the above into easyframes:
```
hhkm = hhkit(df_master)
hhkh = hhkit(df_using_hh)
hhki = hhkit(df_using_ind)

print(hhkm.df)
print(hhkh.df)
print(hhki.df)
```
You can replace the existing dataframe in the hhkit object by passing in a dict or a Pandas DataFrame to the `from_dict` method (even though the method is named `from_dict`, it will still accept a DataFrame object):
```
myhhkit.from_dict(df_master) # If the object already exists, you can replace the existing dataframe. You
# can pass a data frame or a dict to the from_dict() method.
```
## Egen commands

If you are using Stata, and you want to add a column with the household size, the command is simple:

`egen hhsize = count(id), by(hh)`

If you are using Pandas and have the dataset loaded as df, and you are NOT using easyframes, then you might have to do something like:

```
result = df[include].groupby('hh')['hh'].agg(['count'])
result.rename(columns={'count':'hh size'}, inplace=True)
merged = pd.merge(df, result, left_on='hh', right_index=True, how='left')
```

Using the easyframes package, the command would be:

```
from easyframes.easyframes import hhkit

hhkm.egen(operation='count', groupby='hh', col='hh', column_label='hhsize')
print(hhkm.df)
```

and Bob's your uncle:

```
age educ fridge has_car hh house_rooms id male prov weighthh hhsize
0 44 pri yes 1 1 3 1 1 BC 2 3
1 43 bach yes 1 1 3 2 0 BC 2 3
2 13 pri yes 1 1 3 3 1 BC 2 3
3 70 hi no 1 2 2 1 1 Alberta 3 1
4 23 bach yes 0 3 1 1 1 BC 2 2
5 20 sec yes 0 3 1 2 0 BC 2 2
6 37 hi no 1 4 3 1 1 Alberta 3 4
7 35 hi no 1 4 3 2 0 Alberta 3 4
8 8 pri no 1 4 3 3 0 Alberta 3 4
9 15 pri no 1 4 3 4 0 Alberta 3 4
```

Ok, so it doesn't save much typing or space, but suppose you also want to calculate the average age in the household. Here you would simply add the following command
```
hhkm.egen(operation='mean', groupby='hh', col='age', column_label='mean age in hh')
```
and here is the result:
```
age educ fridge has_car hh house_rooms id male prov weighthh hhsize mean age in hh
0 44 pri yes 1 1 3 1 1 BC 2 3 33.333333
1 43 bach yes 1 1 3 2 0 BC 2 3 33.333333
2 13 pri yes 1 1 3 3 1 BC 2 3 33.333333
3 70 hi no 1 2 2 1 1 Alberta 3 1 70.000000
4 23 bach yes 0 3 1 1 1 BC 2 2 21.500000
5 20 sec yes 0 3 1 2 0 BC 2 2 21.500000
6 37 hi no 1 4 3 1 1 Alberta 3 4 23.750000
7 35 hi no 1 4 3 2 0 Alberta 3 4 23.750000
8 8 pri no 1 4 3 3 0 Alberta 3 4 23.750000
9 15 pri no 1 4 3 4 0 Alberta 3 4 23.750000
```
You can also include or exclude certain rows. For example, suppose we want to include in household size only members over the age of 22:
```
hhkm.egen(operation='count', groupby='hh', col='hh', column_label='hhs_o22', include=hhkm.df['age']>22,
varlabel="hhsize including only members over 22 years of age")
print(hhkm.df)
```
The result:
```
age educ fridge has_car hh house_rooms id male prov weighthh hhsize mean age in hh hhs_o22
0 44 pri yes 1 1 3 1 1 BC 2 3 33.333333 2
1 43 bach yes 1 1 3 2 0 BC 2 3 33.333333 2
2 13 pri yes 1 1 3 3 1 BC 2 3 33.333333 2
3 70 hi no 1 2 2 1 1 Alberta 3 1 70.000000 1
4 23 bach yes 0 3 1 1 1 BC 2 2 21.500000 1
5 20 sec yes 0 3 1 2 0 BC 2 2 21.500000 1
6 37 hi no 1 4 3 1 1 Alberta 3 4 23.750000 2
7 35 hi no 1 4 3 2 0 Alberta 3 4 23.750000 2
8 8 pri no 1 4 3 3 0 Alberta 3 4 23.750000 2
9 15 pri no 1 4 3 4 0 Alberta 3 4 23.750000 2
```
You can also exclude members over 22 years of age (just presenting the command, not running it for this demo):
```
hhkm.egen(operation='count', groupby='hh', col='hh', column_label='hhs_o22', exclude=hhkm.df['age']>22,
varlabel="hhsize including only members over 22 years of age")
```
You'll noticed that I added a variable label. Variable labels are discussed below. If you don't specify the column label, then a default is constructed.

Also, there is an option to sepcify what to replace NaNs with. Egen will fill with NaNs observations where the `col` or `groupby` variables contain NaNs (which can happen after `merge`s, for example.) You can specify `replacenanwith` to replace these NaNs with something else, e.g. `replacenanwith = 0`:

```
hhkm.egen(operation='count', groupby='hh', col='hh', column_label='hhs_o22', exclude=hhkm.df['age']>22,
varlabel="hhsize including only members over 22 years of age, replacenanwith = 0" )
```

## Variable labels
Variable labels are supported too.

```
hhkm.set_variable_labels({'hh':'Household ID','id':'Member ID'})
hhkm.sdesc()
```
```
-------------------------------------------------------------------------------------
obs: 10
vars: 13
-------------------------------------------------------------------------------------
Variable Data Type Variable Label
-------------------------------------------------------------------------------------
'age' int64
'educ' object
'fridge' object
'has_car' int64
'hh' int64 Household ID
'house_rooms' int64
'id' int64 Member ID
'male' int64
'prov' object
'weighthh' int64
'hhsize' int64
'mean age in hh' float64
'hhs_o22' int64 hhsize including only members over 22 years of age
```
## Stata-like Merging

There is also a Stata-like merge method, which creates a merge variable for you in the dataset (and copies over the variable labels):
```
hhkm.statamerge(hhkh, on=['hh'], mergevarname='_merge_hh')
print(hhkm.df)
hhkm.sdesc()
```
```
age educ fridge has_car hh house_rooms id male prov weighthh hhsize mean age in hh hhs_o22 has_fence _merge_hh
0 44 pri yes 1 1 3 1 1 BC 2 3 33.333333 2 NaN 1
1 43 bach yes 1 1 3 2 0 BC 2 3 33.333333 2 NaN 1
2 13 pri yes 1 1 3 3 1 BC 2 3 33.333333 2 NaN 1
3 70 hi no 1 2 2 1 1 Alberta 3 1 70.000000 1 1 3
4 23 bach yes 0 3 1 1 1 BC 2 2 21.500000 1 NaN 1
5 20 sec yes 0 3 1 2 0 BC 2 2 21.500000 1 NaN 1
6 37 hi no 1 4 3 1 1 Alberta 3 4 23.750000 2 0 3
7 35 hi no 1 4 3 2 0 Alberta 3 4 23.750000 2 0 3
8 8 pri no 1 4 3 3 0 Alberta 3 4 23.750000 2 0 3
9 15 pri no 1 4 3 4 0 Alberta 3 4 23.750000 2 0 3
10 NaN NaN NaN NaN 5 NaN NaN NaN NaN NaN NaN NaN NaN 1 2
11 NaN NaN NaN NaN 6 NaN NaN NaN NaN NaN NaN NaN NaN 1 2
12 NaN NaN NaN NaN 7 NaN NaN NaN NaN NaN NaN NaN NaN 0 2
-------------------------------------------------------------------------------------
obs: 13
vars: 15
-------------------------------------------------------------------------------------
Variable Data Type Variable Label
-------------------------------------------------------------------------------------
'age' float64
'educ' object
'fridge' object
'has_car' float64
'hh' float64 Household ID
'house_rooms' float64
'id' float64 Member ID
'male' float64
'prov' object
'weighthh' float64
'hhsize' float64
'mean age in hh' float64
'hhs_o22' float64 hhsize including only members over 22 years of age
'has_fence' float64
'_merge_hh' int64
```
Here is another merge, this one replacing the labels in the original/left/master dataset when the same variable appears in both datasets. I will merge an individual-level dataset with the previously merged dataset:
```
hhki.set_variable_labels({'hh':'--> Household ID', 'empl':'Employment status'})
hhkm.statamerge(hhki, on=['hh','id'], mergevarname='_merge_ind')
print(hhkm.df)
hhkm.sdesc()
```
```
age educ fridge has_car hh house_rooms id male prov weighthh has_fence _merge_hh empl _merge_ind
0 44 secondary yes 1 1 3 1 1 BC 2 NaN 1 not employed 3
1 43 bachelor yes 1 1 3 2 0 BC 2 NaN 1 full-time 3
2 13 primary yes 1 1 3 3 1 BC 2 NaN 1 NaN 1
3 70 higher no 1 2 2 1 1 Alberta 3 1 3 part-time 3
4 23 bachelor yes 0 3 1 1 1 BC 2 NaN 1 NaN 1
5 20 secondary yes 0 3 1 2 0 BC 2 NaN 1 NaN 1
6 37 higher no 1 4 3 1 1 Alberta 3 0 3 self-employed 3
7 35 higher no 1 4 3 2 0 Alberta 3 0 3 full-time 3
8 8 primary no 1 4 3 3 0 Alberta 3 0 3 NaN 1
9 15 primary no 1 4 3 4 0 Alberta 3 0 3 NaN 1
10 NaN NaN NaN NaN 5 NaN NaN NaN NaN NaN 1 2 NaN 1
11 NaN NaN NaN NaN 6 NaN NaN NaN NaN NaN 1 2 NaN 1
12 NaN NaN NaN NaN 7 NaN NaN NaN NaN NaN 0 2 NaN 1
13 NaN NaN NaN NaN 1 NaN 4 NaN NaN NaN NaN NaN part-time 2
14 NaN NaN NaN NaN 5 NaN 1 NaN NaN NaN NaN NaN full-time 2
15 NaN NaN NaN NaN 5 NaN 2 NaN NaN NaN NaN NaN part-time 2
16 NaN NaN NaN NaN 4 NaN 5 NaN NaN NaN NaN NaN self-employed 2
------------------------------------------------------------------------
obs: 17
vars: 14
------------------------------------------------------------------------
Variable Data Type Variable Label
------------------------------------------------------------------------
'age' float64
'educ' object
'fridge' object
'has_car' float64
'hh' float64 --> Household ID
'house_rooms' float64
'id' float64 Member ID
'male' float64
'prov' object
'weighthh' float64
'has_fence' float64 This dwelling has a fence
'_merge_hh' float64
'empl' object Employment status
'_merge_ind' int64
```
The `statamerge` method will not overwrite variables if you set `replacelabels=False` in the method (it is set to `True` by default). After a merge, one normally likes to tabulate the merge variable. That is in the next section.

## Stata-like tabulations and cross-tabulations (one-way and two-way)

### One-way tabulations

First, lets tabulate a merge variable. This will be a simple one-way tabulation with no weights or exclusions of rows (though we can exclude rows - this is shown further below):
```
df_tab_m1 = hhkm.tab('_merge_hh', p=True)
df_tab_m2 = hhkm.tab('_merge_ind', p=True)
```
```
count percent
_merge_hh
1.0 5 29.411765
2.0 3 17.647059
3.0 5 29.411765
nan 4 23.529412
total 17 100.000000
count percent
_merge_ind
1 8 47.058824
2 4 23.529412
3 5 29.411765
total 17 100.000000
```
The `p=True` just means to display the output. Lets do a one-way tabulation of education:
```
df_tab = hhkm.tab('educ', p=True)
```
```
count percent
educ
bach 2 11.764706
hi 3 17.647059
pri 4 23.529412
sec 1 5.882353
nan 7 41.176471
total 17 100.000000
```
Now lets make it a bit more interesting: lets add weights, exclude some observations, and use the variable label instead of the variable name:
```
hhkm.set_variable_labels({'educ':'Level of education', 'house_rooms':'Number of rooms in the house'})
df_tab = hhkm.tab('educ', p=True, weightcolumn='weighthh', include=hhkm.df['age'] > 10, usevarlabels=True)
```
```
count percent
Level of education
bach 1.636364 18.181818
hi 3.681818 40.909091
pri 2.863636 31.818182
sec 0.818182 9.090909
total 9.000000 100.000000
```

### Two-way tabulations

For two-way tabulations, just provide an interable (list or set) of variable names as the first argument:
```
df_tab = hhkm.tab(['educ','house_rooms'], decimalplaces=5, usevarlabels=[False, False], p=True)
```
```
Statistic count row percent column percent cell percent
house_rooms 1.0 2.0 3.0 nan total 1.0 2.0 3.0 nan total 1.0 2.0 3.0 nan 1.0 2.0 3.0 nan total
educ
bach 1 0 1 0 2 50 0.00000 50.00000 0 100 50 0 14.28571 NaN 5.88235 0.00000 5.88235 0.00000 11.76471
hi 0 1 2 0 3 0 33.33333 66.66667 0 100 0 100 28.57143 NaN 0.00000 5.88235 11.76471 0.00000 17.64706
pri 0 0 4 0 4 0 0.00000 100.00000 0 100 0 0 57.14286 NaN 0.00000 0.00000 23.52941 0.00000 23.52941
sec 1 0 0 0 1 100 0.00000 0.00000 0 100 50 0 0.00000 NaN 5.88235 0.00000 0.00000 0.00000 5.88235
nan 0 0 0 7 7 0 0.00000 0.00000 100 100 0 0 0.00000 NaN 0.00000 0.00000 0.00000 41.17647 41.17647
total 2 1 7 7 17 NaN NaN NaN NaN NaN 100 100 100.00000 NaN 11.76471 5.88235 41.17647 41.17647 100.00000
```
By default, it will display variable labels instead of variable names:
```
df_tab = hhkm.tab(['educ','house_rooms'], decimalplaces=5, p=True)
```
```
Statistic count row percent column percent cell percent
Number of rooms in the house 1.0 2.0 3.0 nan total 1.0 2.0 3.0 nan total 1.0 2.0 3.0 nan 1.0 2.0 3.0 nan total
Level of education
bach 1 0 1 0 2 50 0.00000 50.00000 0 100 50 0 14.28571 NaN 5.88235 0.00000 5.88235 0.00000 11.76471
hi 0 1 2 0 3 0 33.33333 66.66667 0 100 0 100 28.57143 NaN 0.00000 5.88235 11.76471 0.00000 17.64706
pri 0 0 4 0 4 0 0.00000 100.00000 0 100 0 0 57.14286 NaN 0.00000 0.00000 23.52941 0.00000 23.52941
sec 1 0 0 0 1 100 0.00000 0.00000 0 100 50 0 0.00000 NaN 5.88235 0.00000 0.00000 0.00000 5.88235
nan 0 0 0 7 7 0 0.00000 0.00000 100 100 0 0 0.00000 NaN 0.00000 0.00000 0.00000 41.17647 41.17647
total 2 1 7 7 17 NaN NaN NaN NaN NaN 100 100 100.00000 NaN 11.76471 5.88235 41.17647 41.17647 100.00000
```
Finally, you can do two-way tabulations with weights and excluding selected rows:
```
df_tab = hhkm.tab(['educ','house_rooms'], decimalplaces=5, usevarlabels=[True, True],
p=True, include=hhkm.df['age'] > 10, weightcolumn='weighthh')
```
```
Statistic count row percent column percent cell percent
Number of rooms in the house 1.0 2.0 3.0 total 1.0 2.0 3.0 total 1.0 2.0 3.0 1.0 2.0 3.0 total
Level of education
bach 0.818182 0.000000 0.818182 1.636364 50 0.00000 50.00000 100 50 0 13.33333 9.09091 0.00000 9.09091 18.18182
hi 0.000000 1.227273 2.454545 3.681818 0 33.33333 66.66667 100 0 100 40.00000 0.00000 13.63636 27.27273 40.90909
pri 0.000000 0.000000 2.863636 2.863636 0 0.00000 100.00000 100 0 0 46.66667 0.00000 0.00000 31.81818 31.81818
sec 0.818182 0.000000 0.000000 0.818182 100 0.00000 0.00000 100 50 0 0.00000 9.09091 0.00000 0.00000 9.09091
total 1.636364 1.227273 6.136364 9.000000 NaN NaN NaN NaN 100 100 100.00000 18.18182 13.63636 68.18182 100.00000
```

## Recode/Replace

Stata has `recode` and `replace` commands, which do similar operations. With the EasyFrames hhkit, it is one method:
```
include = pd.Series([True, False, True, False, True, True, False, True,
True, True, False, True, False, True, False, False, True],
index=np.arange(17))
hhkm.rr('educ',{'pri':'primary','sec':'secondary','hi':'higher education','bach':'bachelor'}, include=include)
hhkm.rr('has_fence', {0:2,1:np.nan,np.nan:-1}, include=include)
hhkm.rr('has_car', {0:1,1:0,np.nan:-9}, include=include)
print(hhkm.df)
```
```
age fridge hh house_rooms id male prov weighthh hhsize mean age in hh hhs_o22 _merge_hh empl _merge_ind educ has_fence has_car
0 44 yes 1 3 1 1 BC 2 3 33.333333 2 1 ue 3 primary -1 0
1 43 yes 1 3 2 0 BC 2 3 33.333333 2 1 ft 3 bach NaN 1
2 13 yes 1 3 3 1 BC 2 3 33.333333 2 1 NaN 1 primary -1 0
3 70 no 2 2 1 1 Alberta 3 1 70.000000 1 3 pt 3 hi 1 1
4 23 yes 3 1 1 1 BC 2 2 21.500000 1 1 NaN 1 bachelor -1 1
5 20 yes 3 1 2 0 BC 2 2 21.500000 1 1 NaN 1 secondary -1 1
6 37 no 4 3 1 1 Alberta 3 4 23.750000 2 3 se 3 hi 0 1
7 35 no 4 3 2 0 Alberta 3 4 23.750000 2 3 ft 3 higher education 2 0
8 8 no 4 3 3 0 Alberta 3 4 23.750000 2 3 NaN 1 primary 2 0
9 15 no 4 3 4 0 Alberta 3 4 23.750000 2 3 NaN 1 primary 2 0
10 NaN NaN 5 NaN NaN NaN NaN NaN NaN NaN NaN 2 NaN 1 NaN 1 NaN
11 NaN NaN 6 NaN NaN NaN NaN NaN NaN NaN NaN 2 NaN 1 NaN NaN -9
12 NaN NaN 7 NaN NaN NaN NaN NaN NaN NaN NaN 2 NaN 1 NaN 0 NaN
13 NaN NaN 1 NaN 4 NaN NaN NaN NaN NaN NaN NaN pt 2 NaN -1 -9
14 NaN NaN 5 NaN 1 NaN NaN NaN NaN NaN NaN NaN ft 2 NaN NaN NaN
15 NaN NaN 5 NaN 2 NaN NaN NaN NaN NaN NaN NaN pt 2 NaN NaN NaN
16 NaN NaN 4 NaN 5 NaN NaN NaN NaN NaN NaN NaN se 2 NaN -1 -9
```
There might be more, just have a look at the code (which I need to document better, but hopefully the variable names are helpful). Enjoy!
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