This question already has answers here:
What is the difference between a pandas Series and a single-column DataFrame?
(6 answers)
Closed 3 years ago.
If we perform value_counts function on a column of a Data Frame, it gives us a Series which contains unique values' counts.
The type operation gives pandas.core.series.Series as a result. My question is that what is the basic difference between a Series & a Data Frame?
You can think of Series as a column in a DataFrame while the actual DataFrame is the table if you think of it in terms of sql
Related
This question already has answers here:
How to drop a list of rows from Pandas dataframe?
(15 answers)
Delete a column from a Pandas DataFrame
(20 answers)
Closed 6 months ago.
I am new to both pandas and python in general.
I have a dataset that I have transposed(T) and I want to use the same transposed format to drop some rows and columns.
I am able to transpose in a different window but when I try to drop some rows, it returns untransposed results.
I am looking for something like this(to combine transpose & drop)
datafraw.describe().T, drop(labels =['rowName', index = 1]
When i run the two separately, here is what it seems the transposition seems to be overshadowed by the drop commandtranspositioned table combined drop and transpositioned table
This question already has answers here:
How to pivot a dataframe in Pandas? [duplicate]
(2 answers)
Closed 1 year ago.
Hi there I have a data set look like df1 below and I want to make it look like df2 using pandas. I have tried to use pivot and transpose but can't wrap my head around how to do it. Appreciate any help!
This should do the job
df.pivot_table(index=["AssetID"], columns='MeterName', values='MeterValue')
index: Identifier
columns: row values that will become columns
values: values to put in those columns
I often have the same trouble:
https://towardsdatascience.com/reshape-pandas-dataframe-with-pivot-table-in-python-tutorial-and-visualization-2248c2012a31
This could help next time.
This question already has answers here:
Find the max of two or more columns with pandas
(3 answers)
Pandas: get the min value between 2 dataframe columns
(1 answer)
pandas get the row-wise minimum value of two or more columns
(2 answers)
Closed 2 years ago.
Getting TypeError:"(['guardrails'], ['order_case'])' is an invalid key" error while trying to get min of two columns row wise in pandas but the above 2 columns exists in the dataframe.
Code line:
Master_File['Guardrails View'] = min(Master_File[['guardrails'],['order_case']])
The correct syntax to select multiple columns from a Pandas DataFrame is df[[column1,column2]]. Also, since you are trying to take the row-wise minimum of the two columns, you will want to use the .min function with argument axis=1 (the axis=1 argument is what performs the operation row-wise; the default behavior is column-wise). So in your case, the code would be:
Master_File['Guardrails View'] = Master_File[['guardrails','order_case']].min(axis=1)
which will append the 'Guardrails View' column containing the row-wise minimum of guardrails and order_case to the Master_File DataFrame.
This question already has answers here:
Spark DataFrame equivalent to Pandas Dataframe `.iloc()` method?
(4 answers)
Get a range of columns of Spark RDD
(3 answers)
Closed 3 years ago.
Assuming that I have a Spark Dataframe df, how can I select a range of columns e.g. from column 100 to column 200?
Since df.columns returns a list, you can slice it and pass it to select:
df.select(df.columns[99:200])
This gets the subset of the DataFrame containing the 100th to 200th columns, inclusive.
This question already has answers here:
How to replace NaNs by preceding or next values in pandas DataFrame?
(10 answers)
Closed 3 years ago.
Table 1 represents the format of my raw data. The dataset was prepared in such a way that the name of a variable 1 is only mentioned for the first observation. I am exploring the dataset and would like to report the count of certain features grouped by the first variable. to achieve this I would have to transform my data into the second table (Output).
How can I achieve this with pandas?
1
The solution can be found in the pandas documentation under Upsampling. The method used is called ffill() and is used as such:
df.ffill()