"...
pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Pandas is free software released under the three-clause BSD license.[2] The name is derived from the term "panel data", an econometrics term for multidimensional structured data sets...."Pandas is a library that can easily deal with datasets, and together with numpy and scipy, can solve a great number of hydrology and hydraulics problems.
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Pandas can easily read text/csv files, and can categorize and make operations on its data with few lines of code.
First, we have always to import pandas library with:
To read a csv timeseries of precipitation daily data, we can write:
if the index column is the first one, and it have dates in standard format.
To get average and standard deviation, just write:
And to make an easy and beautiful histogram of this data, just write:
Pandas documentation is available on the site:http://pandas.pydata.org/pandas-docs/stable/install.html
Happy analyzing!
"
Pandas can easily read text/csv files, and can categorize and make operations on its data with few lines of code.
First, we have always to import pandas library with:
import pandas as pd
To read a csv timeseries of precipitation daily data, we can write:
dataSeries = pd.read_csv('csvfile.csv', index_col=0, parse_dates=True)
if the index column is the first one, and it have dates in standard format.
To get average and standard deviation, just write:
m1,d1 = serY.mean(), serY.std()
And to make an easy and beautiful histogram of this data, just write:
dataSeries.hist()
Pandas documentation is available on the site:http://pandas.pydata.org/pandas-docs/stable/install.html
Happy analyzing!
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