[PERF] Performance issue in pandas 1.3.4's read_csv Function with Large Column CSV Files

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**Issue Description:**

Hello.
I have discovered a performance degradation in the `read_csv` function of pandas version 1.3.4 when handling CSV files with a large number of columns. This problem significantly increases the loading time from just a few seconds in the previous version 1.2.5 to several minutes, almost 60x diff. I found some discussions on GitHub related to this issue, including [#44106](https://github.com/pandas-dev/pandas/issues/44106) and [#44192](https://github.com/pandas-dev/pandas/pull/44192).
I noticed that in your CI environment, the `requirements-wheel.txt` file specifies `pandas==1.3.4` for Python version `3.10`. This can lead to increased time and resource consumption in testing, especially when frequently using `pd.read_csv`.
It might be worth considering updating the pandas version or adjusting the dependencies to optimize testing performance. This could help ensure smoother testing processes and resource efficiency.

**Steps to Reproduce:**

I have created a small reproducible example to better illustrate this issue.

```python
# v1.3.4
import os
import pandas
import numpy
import timeit

def generate_sample():
if os.path.exists("test_small.csv.gz") == False:
nb_col = 100000
nb_row = 5
feature_list = {'sample': ['s_' + str(i+1) for i in range(nb_row)]}
for i in range(nb_col):
feature_list.update({'feature_' + str(i+1): list(numpy.random.uniform(low=0, high=10, size=nb_row))})
df = pandas.DataFrame(feature_list)
df.to_csv("test_small.csv.gz", index=False, float_format="%.6f")

def load_csv_file():
col_names = pandas.read_csv("test_small.csv.gz", low_memory=False, nrows=1).columns
types_dict = {col: numpy.float32 for col in col_names}
types_dict.update({'sample': str})
feature_df = pandas.read_csv("test_small.csv.gz", index_col="sample", na_filter=False, dtype=types_dict, low_memory=False)
print("loaded dataframe shape:", feature_df.shape)

generate_sample()
timeit.timeit(load_csv_file, number=1)

# results
loaded dataframe shape: (5, 100000)
120.37690759263933

```

```python
# v1.3.5
import os
import pandas
import numpy
import timeit

def generate_sample():
if os.path.exists("test_small.csv.gz") == False:
nb_col = 100000
nb_row = 5
feature_list = {'sample': ['s_' + str(i+1) for i in range(nb_row)]}
for i in range(nb_col):
feature_list.update({'feature_' + str(i+1): list(numpy.random.uniform(low=0, high=10, size=nb_row))})
df = pandas.DataFrame(feature_list)
df.to_csv("test_small.csv.gz", index=False, float_format="%.6f")

def load_csv_file():
col_names = pandas.read_csv("test_small.csv.gz", low_memory=False, nrows=1).columns
types_dict = {col: numpy.float32 for col in col_names}
types_dict.update({'sample': str})
feature_df = pandas.read_csv("test_small.csv.gz", index_col="sample", na_filter=False, dtype=types_dict, low_memory=False)
print("loaded dataframe shape:", feature_df.shape)

generate_sample()
timeit.timeit(load_csv_file, number=1)

# results
loaded dataframe shape: (5, 100000)
2.8567268839105964

```

**Suggestion**

I would recommend considering an upgrade to a different version of pandas >= 1.3.5 or exploring other solutions to optimize the performance of loading CSV files.
Any other workarounds or solutions would be greatly appreciated.
Thank you!

Start by inspecting requirements-wheel.txt, especially the pandas requirement for Python 3.10, then run the provided read_csv reproduction with pandas 1.3.4 and 1.3.5. Done means selecting a dependency adjustment that avoids the reported slowdown and confirming the large-column CSV benchmark improves without breaking the testing setup.

Written by the indexing model from the issue text.