Pandas | Python package
kandi X-RAY | Pandas Summary
kandi X-RAY | Pandas Summary
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.
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Top functions reviewed by kandi - BETA
- Describe a pandas DataFrame or Series .
- Convert wide index to long .
- Print information about the table .
- Read a JSON file .
- Normalize JSON data .
- Evaluate an expression .
- Return loc indexer .
- Cut an array into bins .
- Merge two DataFrames .
- Create the appropriate axes
Pandas Key Features
Pandas Examples and Code Snippets
Lines of Code : 4License : Permissive (MIT License)
Copydef calculate_loss(original, reconstructed):
return tf.div(tf.reduce_sum(tf.square(tf.sub(reconstructed,
original))),
tf.constant(float(batch_size))) Community Discussions
Trending Discussions on Pandas
QUESTION
I want to add a new column 'BEST' to this dataframe, which contains a list of the names of the columns which meet these criteria:
- Subtract from the current value in each column the value in the row that is 2 rows back
- The column that has the highest result of this subtraction will be listed in 'BEST'
- If more more than one column shares the same highest result, they all get listed
- If all columns have the same result, they all get listed
Input:
...ANSWER
Answered 2021-Jun-16 at 03:33First use shift and subtract to get the diff, then replace the maximum values with the column name and drop the others.
QUESTION
I have a data frame including three columns named 'Altitude', 'Distance', 'Slope'. The column of 'Slope' is calculated using the two first columns 'Altitude', 'Distance'. @ the first step the purpose was to calculate 'Slope' using a condition explained below: A condition function was deployed to start from the top column of the "Distance" variable and add up (sum) values until the summation of them is greater or equal to 10 (>=10). If this condition corrects then calculate the "Slope" using the given formula: Slope=Average(Altitude)/(sum(Distance)). The summation of the 'Distance' was counting from the first value of that to the index that the 'Distance' has stopped there). The following code is for the above explanation (By Tim Roberts):
...ANSWER
Answered 2021-May-19 at 13:38Use this code after you calculate s to get slope column with desired values:
QUESTION
Suppose I have a Pandas dataframe with two identifier columns like this:
...ANSWER
Answered 2021-Jun-15 at 20:56Sounds like a network issue, try with networkx
QUESTION
Consider this dataframe:
...ANSWER
Answered 2021-Jun-15 at 20:30Try:
QUESTION
I have a data frame like this,
...ANSWER
Answered 2021-Jun-11 at 05:56df = df.set_index(["ID", "Rank"])
df = df.unstack("Rank")
df.columns = df.columns.map(lambda col: "_".join(map(str, col)))
QUESTION
I have data which looks like this:
...ANSWER
Answered 2021-Jun-15 at 18:35import numpy as np
features_dict = {
'feat1': np.array([[0,1],[2,3],[4,5]]),
'feat2': np.array([[6,7],[8,9],[10,11]]),
'feat3': np.array([1, 0, 0]),
'feat4': np.array([[1],[2],[1]])
}
ind = features_dict['feat3'] == 0
features_dict = {k: v[ind] for k,v in features_dict.items()}
QUESTION
I have a Graph loaded in pandas and I want to check if my graph has nodes with reciprocity. My dataset looks like this:
id from to 0 s01 s03 1 s02 s01 2 s03 s01The desired output of my code is the reciprocal nodes: (s01, s03)
I found a solution transforming my dataframe into tuples and comparing each combination of my nodes, but I'm sure this solution is far from ideal. Following is my code:
...ANSWER
Answered 2021-Jun-15 at 18:22You can merge the DataFrame with itself after swapping the from and to columns in the right DataFrame. Then sort the merged result and drop duplicates to get the unique pairs of reciprocal nodes.
QUESTION
I am trying to parse many XML test results files and get the necessary data like testcase name, test result, failure message etc to an excel format. I decided to go with Python.
My XML file is a huge file and the format is as follows. The cases which failed has a message, & and the passed ones only has . My requirement is to create an excel with testcasename, test status(pass/fail), test failure message.
...ANSWER
Answered 2021-Jun-15 at 17:46Since your XML is relatively flat, consider a list/dictionary comprehension to retrieve all child elements and attrib dictionary. From there, call pd.concat once outside the loop. Below runs a dictionary merge (Python 3.5+).
QUESTION
I have a dataframe output from the python script which gives following output
Datetime High Low Time 546 2021-06-15 14:30:00 15891.049805 15868.049805 14:30:00 547 2021-06-15 14:45:00 15883.000000 15869.900391 14:45:00 548 2021-06-15 15:00:00 15881.500000 15866.500000 15:00:00 549 2021-06-15 15:15:00 15877.750000 15854.549805 15:15:00 550 2021-06-15 15:30:00 15869.250000 15869.250000 15:30:00i Want to remove all rows where time is equal to 15:30:00. tried different things but unable to do. Help please.
...ANSWER
Answered 2021-Jun-15 at 15:55The way I did was the following,
First we get the the time we want to remove from the dataset, that is 15:30:00 in this case.
Since the Datetime column is in the datetime format, we cannot compare the time as strings. So we convert the given time in the datetime.time() format.
rm_time = dt.time(15,30)
With this, we can go about using the DataFrame.drop()
df.drop(df[df.Datetime.dt.time == rm_time].index)
QUESTION
Situation: I have two dataframes df1 and df2, where df1 has a datetime index based on days, and df2 has two date columns 'wk start' and 'wk end' that are weekly ranges as well as one data column 'statistic' that stores data corresponding to the week range.
What I would like to do: Add to df1 a column for 'statistic' whereby I lookup each date (on a daily basis, i.e. each row) and try to find the corresponding 'statistic' depending on the week that this date falls into.
I believe the answer would require merging df2 into df1 but I'm lost as to how to proceed after that.
Appreciate any help you might provide! Thanks!
df1: (note: I skipped the rows between 2019-06-12 and 2019-06-16 to keep the example short.)
age date 2019-06-10 20 2019-06-11 21 2019-06-17 19 2019-06-18 18df2:
wk start wk end statistic 2019-06-10 2019-06-14 102 2019-06-17 2019-06-21 100 2019-06-24 2019-06-28 547 2019-07-02 2019-07-25 268Desired output:
age statistic date :--- :-------- 2019-06-10 20 102 2019-06-11 21 102 2019-06-17 19 100 2019-06-18 18 100code for the dataframes d1 and d2
...ANSWER
Answered 2021-Jun-15 at 09:37You could loop through the dataframe and subset the second dataframe as you go.
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