Shantanu's Blog
Database ConsultantMay 05, 2024
Pandas as command prompt
You can use pandas at command prompt like this...
curl -s https://www.ecb.europa.eu/stats/eurofxref/eurofxref-hist.zip | \
gunzip | \
python3 -c 'import sys, pandas as pd
pd.read_csv(sys.stdin).melt("Date").to_csv(sys.stdout, index=False)'
curl -s https://www.ecb.europa.eu/stats/eurofxref/eurofxref-hist.zip | \
gunzip | \
python3 -c 'import sys, pandas as pd
pd.read_csv(sys.stdin).iloc[:, :-1].melt("Date")\
.to_csv(sys.stdout, index=False)'
Labels: pandas, shell script
July 21, 2023
langchain for pandas
langchain is a module to query pandas dataframe using Natural Language. It uses chatGPT to build pandas commands!
!pip install langchain
import os
os.environ["OPENAI_API_KEY"] = "XXXX"
from langchain.agents import create_pandas_dataframe_agent
from langchain.llms import OpenAI
import pandas as pd
pd_agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)
pd_agent.run("Find the total sales for each product line in the year 2003")
_____
Something similar...
# https://github.com/gventuri/pandas-ai
!pip install pandasai
from pandasai import SmartDataframe, SmartDatalake
from pandasai.llm import OpenAI
llm = OpenAI(api_token="YOUR TOKEN")
sdf = SmartDataframe(df, config={"llm": llm})
sdf.chat("Return the top 5 countries by GDP")
sdf.chat("Plot a chart of the gdp by country")
print(sdf.last_code_generated)
If you have more than one dataframe, then use SmartDatalake method and supply a list of dataframes. For e.g.
sdf = SmartDatalake([df, df2, df3], config={"llm": llm})
Labels: machine_learning, openai, pandas, usability
March 16, 2022
Interactive pandas dataframe
iTables is an important utility for pandas dataframe. It will make the df or series interactive.
https://github.com/mwouts/itables
Install the package with:
pip install itables
Activate the interactive mode:
from itables import init_notebook_mode
init_notebook_mode(all_interactive=True)
or use itables.show to show just one Series or DataFrame as an interactive table.
_____
1) At the moment itables does not have an offline mode. While the table data is embedded in the notebook, the jquery and datatables.net are loaded from a CDN.
2) When the data in a table is larger than maxBytes, which is equal to 64KB by default, itables will display only a subset of the table. To show the table in full, modify the value of maxBytes either locally:
show(df, maxBytes=0)
or globally:
import itables.options as opt
opt.maxBytes = 2 ** 20
Labels: pandas, python, usability
July 07, 2021
pandas case study 34
moving average for the variable length window
How do I calculate the moving average where the set of previous rows is not fixed? For e.g. this SQL query will calculate the average for the totalprice column for the last 1 month orders.
SELECT avg(totalprice) OVER (
PARTITION BY custkey
ORDER BY orderdate
RANGE BETWEEN interval '1' month PRECEDING AND CURRENT ROW)
FROM orders
I have this dataframe:
from io import StringIO
import pandas as pd
myst="""cust_1,2020-10-10,100
cust_2,2020-10-10,15
cust_1,2020-10-15,200
cust_1,2020-10-16,240
cust_2,2020-12-20,25
cust_1,2020-12-25,140
cust_2,2021-01-01,5
"""
u_cols=['custkey', 'orderdate', 'totalprice']
myf = StringIO(myst)
import pandas as pd
orders = pd.read_csv(StringIO(myst), sep=',', names = u_cols)
orders['orderdate'] = pd.to_datetime(orders['orderdate'])
df=df.sort_values(list(df.columns))
Answer:
orders['my_average'] = (orders.groupby('custkey')
.apply(lambda d: d.rolling('30D', on='orderdate')['totalprice'].mean())
.reset_index(level=0, drop=True)
.astype(int)
)
Labels: pandas
May 18, 2021
Pandas case study 33
Let's assume we have 2 dataframes of english words.
The words may or may not be the same in both tables and the first dataframe has second column called "count". How do I merge these 2 dataframes and still get to know if a word is from first or second dataframe?
from io import StringIO
u_cols = ['word','count']
audit_trail = StringIO('''
test 1
testing 24
again 52
begin 6
''')
oscar = pd.read_csv(audit_trail, sep=" ", names = u_cols)
_____
from io import StringIO
u_cols = ['word' ]
audit_trail = StringIO('''
newer
age
computing
begin
''')
ptg = pd.read_csv(audit_trail, sep=" ", names = u_cols)
Use full outer join from merge method of pandas.
Do not forget to turn indicators on!
ndf=pd.merge(oscar,ptg,on="word", how="outer", indicator=True)
Labels: pandas
April 29, 2021
Manage your csv using clever csv
Command to analyze the csv file and let us know about delimiter/ escaping.
$ clevercsv detect ./imdb.csv
Detected: SimpleDialect(',', '', '\\')
We can import the csv in pandas dataframe without using read_csv method!
$ clevercsv explore -p imdb.csv
>>> df
The code to create pandas dataframe using clever csv
$ clevercsv code -p ./imdb.csv
import clevercsv
df = clevercsv.read_dataframe("./imdb.csv", delimiter=",", quotechar="", escapechar="\\")
If you are using Jupyter Notebook, use this code...
import clevercsv
rows = clevercsv.read_table('./imdb.csv')
df = clevercsv.read_dataframe('./imdb.csv')
December 13, 2020
get elastic data into pandas dataframe
This 2 lines of code will conncet to Elastic instance hosted in Amazon cloud and download the data from a given index. It will save the data as pandas data frame and also flatten the JSON data to look like an excel spreadsheet.
import eland as ed
df = ed.DataFrame("https://xxx.us-east-1.es.amazonaws.com:443", es_index_pattern="cwl-2020.12.13")
September 18, 2020
Remove junk from pandas dataframe
Non-latin unicode characters in pandas dataframe are a big problem. Several hours are lost cleaning the data when some obscure characters are found in the dataframe imported from the csv or excel file. Here is an easy solution...
from unicodedata import normalize
def clean_normalize_whitespace(x):
if isinstance(x, str):
return normalize('NFKC', x).strip()
else:
return x
df_GDP = df_GDP.applymap(clean_normalize_whitespace)
#clean column headings as well
df_GDP.columns = df_GDP.columns.to_series().apply(clean_normalize_whitespace)
I have used NFKC parameter that stands for Normal Form Kompatibility Composition. The other one is NFKD (Decompostion) along with NFD as well as NFC.
https://unicode.org/reports/tr15/#Norm_Forms
July 13, 2020
Understanding your data
# Download the csv file from kaggle:
https://www.kaggle.com/dorbicycle/world-foodfeed-production
import pandas as pd
food_df = pd.read_csv('FAO.csv' , encoding='latin1')
food_df = food_df.drop(columns=food_df.columns[10:])
I will now import auto normalize class from featuretools. This will detect the internal relationships between columns and show us a nice graph.
from featuretools.autonormalize import autonormalize as an
entityset = an.auto_entityset(food_df)
entityset.plot()
Entities:
Element Code_Item Code_Area Code [Rows: 21477, Columns: 4]
Element Code [Rows: 2, Columns: 2]
Item Code [Rows: 117, Columns: 2]
Area Code [Rows: 174, Columns: 5]
Area Abbreviation [Rows: 169, Columns: 2]
Relationships:
Element Code_Item Code_Area Code.Area Code -> Area Code.Area Code
Element Code_Item Code_Area Code.Item Code -> Item Code.Item Code
Element Code_Item Code_Area Code.Element Code -> Element Code.Element Code
Area Code.Area Abbreviation -> Area Abbreviation.Area Abbreviation
Do not forget to check featuretools module as well. This will add new columns to your dataframe those can be useful in building machine learning module.
import featuretools as ft
fm, features = ft.dfs(entityset=entityset, target_entity='Element Code_Item Code_Area Code')
print (fm)
Labels: machine_learning, pandas, usability
May 01, 2020
Pandas case study 32
Outliers can be removed or adjusted using statistical methods of IQR, Z-Score and Data Smoothing.
1) For calculating IQR (Inter Quartile Range) of a dataset, first calculate it’s 1st Quartile(Q1) and 3rd Quartile(Q3) i.e. 25th and 75 percentile of the data and then subtract Q1 from Q3
import pandas as pd
data = [-2,8,13,19,34,49,50,53,59,64,87,89,1456]
df = pd.DataFrame(data)
df.columns = ['values']
ndf=df.describe().T
ndf['75%'] - ndf['25%']
# returns 45
For finding out the Outlier using IQR we have to define a multiplier which is 1.5 ideally that will decide how far below Q1 and above Q3 will be considered as an Outlier.
higher_limit = ndf['75%'] + 1.5 * 45
lower_limit = ndf['25%'] - 1.5 * 45
df[(df['values'] > higher_limit[0]) | (df['values'] < lower_limit[0])]
2) Z-Score tells how far a point is from the mean of dataset in terms of standard deviation. An absolute value of z score which is above 3 is considered as an outlier.
from scipy import stats
df['z_score']=stats.zscore(df['values'])
df[df['z_score'] > 3]
# returns values z_score
12 1456 3.454979
3) Data smoothing is a process to adjust the spikes and peaks. If your current value if 13 and previous value is 8 and smoothing level is 0.6 then the smoothed value is 11 given by
13*0.6 + (1-0.6)*8
Pandas smoothing function (EWM) can be used to calculate the exponential weighted Moving Average at different alpha levels.
df['ewm_alpha_1']=df['values'].ewm(alpha=0.1).mean()
df['ewm_alpha_3']=df['values'].ewm(alpha=0.3).mean()
df['ewm_alpha_6']=df['values'].ewm(alpha=0.6).mean()
df
https://kanoki.org/2020/04/23/how-to-remove-outliers-in-python/
Labels: machine_learning, pandas
April 16, 2020
Download and unzip any file using python
! wget https://github.com/d6t/d6tstack/raw/master/test-data.zip
! unzip -o test-data.zip
But it can also be done using python code as shown below!
import urllib.request
import zipfile
cfg_fname_sample = "test-data.zip"
urllib.request.urlretrieve(
"https://github.com/d6t/d6tstack/raw/master/" + cfg_fname_sample, cfg_fname_sample
)
zip_ref = zipfile.ZipFile(cfg_fname_sample, "r")
zip_ref.extractall(".")
zip_ref.close()
Labels: pandas, python, usability
April 10, 2020
Numpy Basics
import numpy as np
mylist = [[100, 2, 3], [4, 5786, 6]]
a = np.array(mylist)
a
np.ravel(a)
np.append(a, [2])
np.append(a, [10, 11, 12])
np.append(a, [""])
b = np.array([[400], [800]])
np.append(a, b, axis=1)
np.append(a, [[50, 60, 70]], axis=0)
np.insert(a, 2, [1, 2, 34])
a[:1]
a[0][2]
a.size
a.shape
np.where(a == 3)
np.sort(a, axis=1)
np.sort(a, axis=0)
a.tolist()
np.delete(a, 1, axis=0)
np.delete(a, 1, axis=1)
March 28, 2020
Pandas case study 31
If my data looks like this, how do I create a document per user?
Name Sem Subject Grade
0 John Sem1 Mathematics A
1 Sara Sem1 Biology B
2 John Sem2 Biology A+
3 Sara Sem2 Mathematics B++
In this case, John and Sara are the two users who appear for 2 semisters. I need a nested json document for each user.
John's data will look like this...
{
"John": {
"Sem1": {
"Subject": "Mathematics",
"Grade": "A"
},
"Sem2": {
"Subject": "Biology",
"Grade": "A+"
}
}
}
Here is how to create the dataframe:
df = pd.DataFrame(
{
"Name": ["John", "Sara", "John", "Sara"],
"Sem": ["Sem1", "Sem1", "Sem2", "Sem2"],
"Subject": ["Mathematics", "Biology", "Biology", "Mathematics"],
"Grade": ["A", "B", "A+", "B++"],
}
)
This is obviously a groupby problem. But we also need to export it to dictionary. Dictionary comprehension will make sure to include all users in a single document.
ppdict = {
n: grp.loc[n].to_dict("index")
for n, grp in df.set_index(["Name", "Sem"]).groupby(level="Name")
}
In order to display the data correctly, use json module like this...
import json
print(json.dumps(ppdict, indent=2))
The output will look like this...
{
"John": {
"Sem1": {
"Subject": "Mathematics",
"Grade": "A"
},
"Sem2": {
"Subject": "Biology",
"Grade": "A+"
}
},
"Sara": {
"Sem1": {
"Subject": "Biology",
"Grade": "B"
},
"Sem2": {
"Subject": "Mathematics",
"Grade": "B++"
}
}
}
https://kanoki.org/2020/03/24/convert-pandas-dataframe-to-dictionary/
Labels: pandas
March 14, 2020
Pandas case study 30
url = "https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD&bom=true&format=true&delimiter=%3B"
from urllib.request import urlretrieve
urlretrieve(url, "data.csv")
import pandas as pd
df = pd.read_csv("data.csv", delimiter=";", index_col="Date", parse_dates=True)
%matplotlib inline
df.resample("w").sum().plot()
# https://www.youtube.com/watch?v=_ZEWDGpM-vM&list=WL&index=98&t=23s
Labels: pandas
March 13, 2020
Visual pandas - bamboolib
1) Get your free 14 days trial key.
https://bamboolib.8080labs.com/
2) Install required python package:
pip install bamboolib
jupyter nbextension enable --py qgrid --sys-prefix
jupyter nbextension enable --py widgetsnbextension --sys-prefix
jupyter nbextension install --py bamboolib --sys-prefix
jupyter nbextension enable --py bamboolib --sys-prefix
3) Restart docker container:
4) Start exploring visual pandas!
import bamboolib as bam
import pandas as pd
df = pd.read_csv(bam.titanic_csv)
df
_____
import modules automatically when required!
pip install pyforest
conda install nodejs
python -m pyforest install_extensions
Restart docker container.
February 29, 2020
Pandas case study 29
df = pd.DataFrame(dict(a=[0.1, 0.2, 0.2],
b=[None, 0.1, None],
c=[0.1, None, 0.1])
lead_na = (df.isnull() == False).cumsum() == 0
trail_na = (df.iloc[::-1].isnull() == False).cumsum().iloc[::-1] == 0
trail_lead_nas = top_na | trail_na
Any ideas how this could be expressed more efficiently?
Answer:
df.ffill().isna() | df.bfill().isna()
https://stackoverflow.com/questions/59820159/identify-leading-and-trailing-nas-in-pandas-dataframe
Labels: pandas
Pandas case study 28
frame1=pd.DataFrame(np.random.randn(10).reshape(5,2),index=range(5),columns=['a','b'])
picked_values=pd.Series(['a','a','a','b','b'])
Frame1
a b
0 0.283519 1.462209
1 -0.352342 1.254098
2 0.731701 0.236017
3 0.022217 -1.469342
4 0.386000 -0.706614
Trying to get to the series:
0 0.283519
1 -0.352342
2 0.731701
3 -1.469342
4 -0.706614
I was hoping values[picked_values] would work, but this ends up with five columns.
Answer:
pd.Series(frame1.lookup(picked_values.index,picked_values))
https://stackoverflow.com/questions/59898266/select-columns-in-a-dataframe-conditional-on-row
Labels: pandas
Pandas case study 27
dataframe1 =
In AA BB CC
0 10 1 0
1 11 2 3
2 10 6 0
3 9 1 0
4 10 3 1
5 1 2 0
now I want to create a dataframe that gives me the count of modes for each column, for column AA the count is 3 for mode 10, for columns CC the count is 4 for mode 0, but for BB there are two modes 1 and 2, so for BB I want the sum of counts for the modes. so for BB the count is 2+2=4, for mode 1 and 2.
Therefore the final dataframe that I want looks like below.
Columns Counts
AA 3
BB 4
CC 4
How to do it?
Answer:
You can compare columns with modes and count matches by sum:
df = pd.DataFrame({'Columns': df.columns,
'Val':[df[x].isin(df[x].mode()).sum() for x in df]})
print (df)
Columns Val
0 AA 3
1 BB 4
2 CC 4
https://stackoverflow.com/questions/59874756/counting-mode-occurrences-for-all-columns-in-a-dataframe
Labels: pandas
Pandas case study 26
my_list_of_dicts = [{
'id': 3,
'name': 'John'
},{
'id': 5,
'name': 'Peter'
},{
'id': 2,
'name': 'Peter'
},{
'id': 6,
'name': 'Mariah'
},{
'id': 7,
'name': 'John'
},{
'id': 1,
'name': 'Louis'
}
]
Answer:
df = pd.DataFrame(my_list_of_dicts)
df[df.name.isin(df[df.name.duplicated()]['name'])].to_json(orient='records')
https://stackoverflow.com/questions/59822973/keep-duplicates-by-key-in-a-list-of-dictionaries/60465827#60465827
Labels: pandas
Pandas case study 25
df = pd.DataFrame(data = {'RecordID' : [1,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5],
'DisplayLabel' : ['Source','Test','Value 1','Value 2','Value3','Source','Test','Value 1','Value 2','Source','Test','Value 1','Value 2','Source','Test','Value 1','Value 2','Source','Test','Value 1','Value 2'],
'Value' : ['Web','Logic','S','I','Complete','Person','Voice','>20','P','Mail','OCR','A','I','Dictation','Understandable','S','I','Web','Logic','R','S']})
I am trying to "unmelt" though not exactly the source and test columns into new dataframe.
Answer 1: mask, pivot and join
mask = df['DisplayLabel'].str.contains('Value')
df2 = df[~mask].pivot(index='RecordID', columns='DisplayLabel', values='Value')
dfpiv = (
df[mask].rename(columns={'DisplayLabel':'Result'})
.set_index('RecordID')
.join(df2)
.reset_index()
)
Answer 2: set_index, unstack, then melt
df.set_index(['RecordID', 'DisplayLabel']).Value.unstack().reset_index() \
.melt(['RecordID', 'Source', 'Test'], var_name='Result', value_name='Value') \
.sort_values('RecordID').dropna(subset=['Value'])
https://stackoverflow.com/questions/59847074/unmelt-only-part-of-a-column-from-pandas-dataframe
Labels: pandas
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