You can write the table straight into a .docx file using the python-docx library. If you are using the Conda or installed Python using Anaconda, you can run the command from the command line: conda install python-docx –channel conda-forge Or to pip install from the command line: pip install python-docx After that is installed, we … Read more
If you change this line in your function: dfItem = jsonToDataFrame(data) to: dfItem = pd.DataFrame.from_records(data) it should work. I tested your function with this line replaced, using [‘INAG’] as a parameter passed to your getFinanceHistoricalStockFromByma function, and it returned a DataFrame.
You are not using the correct sum function but the built-in function sum (by default). So the reason why the build-in function won’t work is that’s it takes an iterable as an argument where as here the name of the column passed is a string and the built-in function can’t be applied on a string. … Read more
As of Dask 2.0.0 you may call .repartition(partition_size=”100MB”). This method performs an object-considerate (.memory_usage(deep=True)) breakdown of partition size. It will join smaller partitions, or split partitions that have grown too large. Dask’s Documentation also outlines the usage.
df = pd.DataFrame([[‘Jhon’,15,’A’],[‘Anna’,19,’B’],[‘Paul’,25,’D’]]) df. columns = [‘Name’,’Age’,’Grade’] df Out[472]: Name Age Grade 0 Jhon 15 A 1 Anna 19 B 2 Paul 25 D You can get the index of your row: i = df[((df.Name == ‘jhon’) &( df.Age == 15) & (df.Grade == ‘A’))].index and then drop it: df.drop(i) Out[474]: Name Age Grade 1 … Read more
Use dropna with parameter subset for specify column for check NaNs: data = data.dropna(subset=[‘sms’]) print (data) id city department sms category 1 2 lhr revenue good 1 Another solution with boolean indexing and notnull: data = data[data[‘sms’].notnull()] print (data) id city department sms category 1 2 lhr revenue good 1 Alternative with query: print (data.query(“sms … Read more
You can transpose the single-row dataframe (which still results in a dataframe) and then squeeze the results into a series (the inverse of to_frame). df = pd.DataFrame([list(range(5))], columns=[“a{}”.format(i) for i in range(5)]) >>> df.squeeze(axis=0) a0 0 a1 1 a2 2 a3 3 a4 4 Name: 0, dtype: int64 Note: To accommodate the point raised by … Read more
By using apply df.groupby([‘col1’, ‘col2’])[“col3”, “col4”].apply(lambda x : x.astype(int).sum()) Out[1257]: col3 col4 col1 col2 a c 2 4 d 1 2 b d 1 2 e 2 4 If you want to agg df.groupby([‘col1’, ‘col2’]).agg({‘col3′:’sum’,’col4′:’sum’})
The column must be a datetime dtype, for example after using pd.to_datetime. Then, you can use tz_localize to change the time zone, a naive timestamp corresponds to time zone None: testdata[‘time’].dt.tz_localize(None) Unless the column is an index (DatetimeIndex), the .dt accessor must be used to access pandas datetime functions.