Create Dataframe from a Matrix

Create dataframe from a matrix

If you change your time column into row names, then you can use as.data.frame(as.table(mat)) for simple cases like this.

Example:

data <- c(0.1, 0.2, 0.3, 0.3, 0.4, 0.5)
dimnames <- list(time=c(0, 0.5, 1), name=c("C_0", "C_1"))
mat <- matrix(data, ncol=2, nrow=3, dimnames=dimnames)
as.data.frame(as.table(mat))
  time name Freq
1    0  C_0  0.1
2  0.5  C_0  0.2
3    1  C_0  0.3
4    0  C_1  0.3
5  0.5  C_1  0.4
6    1  C_1  0.5

In this case time and name are both factors. You may want to convert time back to numeric, or it may not matter.

Creating a Pandas DataFrame from a Numpy array: How do I specify the index column and column headers?

You need to specify data, index and columns to DataFrame constructor, as in:

>>> pd.DataFrame(data=data[1:,1:],    # values
...              index=data[1:,0],    # 1st column as index
...              columns=data[0,1:])  # 1st row as the column names

edit: as in the @joris comment, you may need to change above to np.int_(data[1:,1:]) to have correct data type.

How to create the matrix from data frame?

We can use acast

library(reshape2)
acast(df1, year~site, value.var="SLP", mean)

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Or using tapply from base R

with(df1, tapply(SLP, list(year, site), FUN = mean))

How to convert matrix to pandas data frame

As already said your are not creating a matrix but a python dictionary. However a dict can serve as parameter to create a dataframe, but you reversed the indexing order.

import pandas as pd

matrixA={}
matrixA['a']=[0,0]
matrixA['b']=[0,1]

pd.DataFrame(matrixA)

   a  b
0  0  0
1  0  1

Additionally you can use numpys matrix

import numpy as np

a = np.matrix('1 2; 3 4')
pd.DataFrame(a)

   0  1
0  1  2
1  3  4

How to create dataframe based on matrix?

Set index and columns names by df1, df2:

res.index = df1[:len(res.index)]
res.columns = df2[:len(res.columns)]

And then reshape by DataFrame.melt:

df = res.rename_axis(index='df1', columns='df2').melt(ignore_index=False)

Or DataFrame.stack:

df = res.rename_axis(index='df1', columns='df2').stack().reset_index(name='value')

Create a dataframe from arrays python

This should do the trick for you.

columns = ["A", "B", "C"]
rows = ["D", "E", "F"]
data = np.array([[1, 2, 2], [3, 3, 3],[4, 4, 4]])
df = pd.DataFrame(data=data, index=rows, columns=columns)

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