Jul 1, 2012 · Pass the items of the dictionary to the DataFrame constructor, and give the column names. After that parse the Date column to get Timestamp values. Note the difference between python 2.x and 3.x: In python 2.x: df = pd.DataFrame(data.items(), columns=['Date', 'DateValue']) df['Date'] = pd.to_datetime(df['Date'])

pd.DataFrame converts the list of rows (where each row is a scalar value) into a DataFrame. If your function yields DataFrames instead, call pd.concat. Pros of this approach: It is always cheaper to append to a list and create a DataFrame in one go than it is to create an empty DataFrame (or one of NaNs) and append to it over and over again.

df.astype(str) is useful if you are trying to fill missing data (empty cells) in your dataFrame. For instance, if you are trying to query data within a dataFrame column that contains empty cells, the queries might not act as expected since the column data will likely be treated as an object type.

Mar 24, 2014 · In order to test some functionality I would like to create a DataFrame from a string. Let's say my test data looks like: TESTDATA="""col1;col2;col3 1;4.4;99 2;4.5;200 3;4.7;65 4;3.2;140 """ What is the simplest way to read that data into a Pandas DataFrame?

Mar 11, 2016 · If a split along some dimension is necessary due, to, e.g., performance, you can combine DataFrames better with standard Python data structures: A dictionary mapping each year to a Dataframe with the other three dimensions. Three DataFrames, one for each origin, each having three dimensions.

Dec 28, 2018 · I'm try to construct a dataframe (I'm using Pandas library) from some arrays and one matrix. in particular, if I have two array like this: A=[A,B,C] B=[D,E,F] And one matrix like this : 1 2 2 ...

Jun 18, 2014 · Create a three dimensional dataframe (python) 2. Creating a 3D pandas dataframe by using 2 data frames. 0.

Apr 3, 2017 · DataFrame.from_records treats string as a character list. so it needs as many columns as length of string. You could simply use the DataFrame constructor. In [3]: pd.DataFrame(q_list, columns=['q_data']) Out[3]: q_data 0 112354401 1 116115526 2 114909312 3 122425491 4 131957025 5 111373473

import decimal import pyodbc #just corrected a typo here import numpy as np import pandas cnn, cur = myConnectToDBfunction() cmd = "SELECT * FROM myTable" cur.execute(cmd) dataframe = __processCursor(cur, dataframe=True) def __processCursor(cur, dataframe=False, index=None): ''' Processes a database cursor with data on it into either a ...

Nov 6, 2014 · Well, I was wondering if we could use python's multi-dimensional array. Yes, you can use python matrix (as mentioned in the python official docs) or multi-dimensional arrays and convert into pandas DataFrame. import pandas as pd matrix = [ ["a", 1], ["b", 2] ] pd.DataFrame(matrix) 0 1 0 a 1 1 b 2